feat: complete 15-minute optimization support

Anleitung.txt

@@ -0,0 +1,3 @@
+http://192.168.1.175:8503/v1/measurement/keys -> Keys auslesen
+grid_import_mr
+grid_export_mr

CHANGELOG.md

@@ -20,6 +20,12 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
   default 3600 s interval keeps the previous hourly behaviour. The new sub-hourly PV
   providers (pvnode, Forecast.Solar, Solcast) feed their native resolution straight
   into the quarter-hour grid.
+  - Legacy hourly API inputs are normalized onto the quarter-hour grid: PV/load energy
+    is distributed across four slots, prices are held constant, and hourly warm-start
+    solutions are expanded to slot controls. Native slot arrays are preserved and
+    ambiguous lengths are rejected.
+  - Home-appliance scheduling remains hourly and is therefore rejected for sub-hourly
+    optimization instead of being simulated with incorrect slot indices.
   - The Tibber electricity price provider now requests native 15-minute exchange prices
     (`priceInfoRange(resolution: QUARTER_HOURLY)`) and stores them at their native
     resolution, so both the hourly and the 15-minute optimizer are fed the correct

docs/akkudoktoreos/optimauto.md

@@ -149,6 +149,13 @@ The energy management can be run in three modes:
 Use `900` together with a 15-minute electricity price source (for example a dynamic or
 exchange-priced tariff) to let the optimizer schedule on a quarter-hour grid. Keeping the
 default `3600` preserves the previous hourly behaviour.
+
+Legacy hourly API input is accepted at `900`: energy values are distributed over four slots,
+while electricity prices and feed-in tariffs are held constant. Native quarter-hour arrays are
+used unchanged. Other input lengths are rejected to prevent a shortened simulation horizon.
+
+Home-appliance scheduling currently remains hourly and is not supported with a sub-hourly
+optimization interval.
 :::
 
 #### Genetic Algorithm Parameters

src/akkudoktoreos/core/ems.py

@@ -96,8 +96,8 @@ class EnergyManagement(
 
         If no datetime is provided, the current datetime is used.
 
-        The start datetime is always rounded down to the nearest hour
+        The start datetime is rounded down to the configured optimization
-        (i.e., setting minutes, seconds, and microseconds to zero).
+        interval. For a 15-minute grid this yields :00, :15, :30 or :45.
 
         Args:
             start_datetime (Optional[DateTime]): The datetime to set as the start.
@@ -108,7 +108,12 @@ class EnergyManagement(
         """
         if start_datetime is None:
             start_datetime = to_datetime()
-        cls._start_datetime = start_datetime.set(minute=0, second=0, microsecond=0)
+        interval_s = int(cls.config.optimization.interval or 3600)
+        wall_clock_s = (
+            start_datetime.hour * 3600 + start_datetime.minute * 60 + start_datetime.second
+        )
+        remainder_s = wall_clock_s % interval_s
+        cls._start_datetime = start_datetime.subtract(seconds=remainder_s).set(microsecond=0)
         return cls._start_datetime
 
     @classmethod

src/akkudoktoreos/devices/genetic/battery.py

@@ -58,6 +58,8 @@ class Battery:
             self.max_charge_power_w = self.capacity_wh  # TODO this should not be equal capacity_wh
         self.discharge_array = np.full(self.prediction_hours, 0)
         self.charge_array = np.full(self.prediction_hours, 0)
+        self._discharged_raw_wh_per_slot = np.zeros(self.prediction_hours, dtype=float)
+        self._charged_raw_wh_per_slot = np.zeros(self.prediction_hours, dtype=float)
         self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
         self.min_soc_wh = (self.min_soc_percentage / 100) * self.capacity_wh
         self.max_soc_wh = (self.max_soc_percentage / 100) * self.capacity_wh
@@ -101,6 +103,17 @@ class Battery:
         self.soc_wh = min(self.soc_wh, self.max_soc_wh)  # Only clamp to max
         self.discharge_array = np.full(self.prediction_hours, 0)
         self.charge_array = np.full(self.prediction_hours, 0)
+        self._discharged_raw_wh_per_slot = np.zeros(self.prediction_hours, dtype=float)
+        self._charged_raw_wh_per_slot = np.zeros(self.prediction_hours, dtype=float)
+
+    def remaining_discharge_energy_wh(self, hour: int) -> float:
+        """Return DC energy still deliverable within one optimization slot."""
+        raw_power_budget_wh = self.max_charge_power_w * self.slot_duration_h
+        raw_power_remaining_wh = max(
+            raw_power_budget_wh - self._discharged_raw_wh_per_slot[hour], 0.0
+        )
+        raw_soc_available_wh = max(self.soc_wh - self.min_soc_wh, 0.0)
+        return min(raw_power_remaining_wh, raw_soc_available_wh) * self.discharging_efficiency
 
     def set_discharge_per_hour(self, discharge_array: np.ndarray) -> None:
         """Sets the discharge values for each hour."""
@@ -151,8 +164,9 @@ class Battery:
         # Maximum raw discharge due to power limit, scaled to the slot duration.
         # max_charge_power_w is a power [W]; energy movable in one slot is
         # power x slot_duration_h.
-        max_raw_wh = (
+        max_raw_wh = max(
-            self.max_charge_power_w * self.slot_duration_h
+            self.max_charge_power_w * self.slot_duration_h - self._discharged_raw_wh_per_slot[hour],
+            0.0,
         )  # TODO rename to max_discharge_power_w
 
         # Actual raw withdrawal (internal)
@@ -170,6 +184,7 @@ class Battery:
         # Update SoC
         self.soc_wh -= raw_used_wh
         self.soc_wh = max(self.soc_wh, self.min_soc_wh)
+        self._discharged_raw_wh_per_slot[hour] += raw_used_wh
 
         # Losses
         losses_wh = raw_used_wh - delivered_wh
@@ -246,7 +261,10 @@ class Battery:
         soc_wh_fast = self.soc_wh
         # Scale the power cap [W] to a per-slot energy cap [Wh] (W x slot hours).
         # At slot_duration_h=1.0 (hourly) this equals the legacy power value.
-        max_charge_per_slot_wh_fast = self.max_charge_power_w * self.slot_duration_h
+        max_charge_per_slot_wh_fast = max(
+            self.max_charge_power_w * self.slot_duration_h - self._charged_raw_wh_per_slot[hour],
+            0.0,
+        )
         charging_efficiency_fast = self.charging_efficiency
 
         # Decide mode & determine raw_request_wh and raw_charge_wh
@@ -290,6 +308,7 @@ class Battery:
             )
 
         self.soc_wh = new_soc
+        self._charged_raw_wh_per_slot[hour] += raw_input_wh
         losses_wh = raw_input_wh - stored_wh
 
         return stored_wh, losses_wh

src/akkudoktoreos/devices/genetic/inverter.py

@@ -74,9 +74,12 @@ class Inverter:
                 grid_import = -remaining_power  # Negative indicates feeding into the grid
                 self_consumption = self.max_power_wh
             else:
-                # Calculate scr using cached results per energy management/optimization run
+                # Calculate scr using cached results per energy management/optimization run.
+                # The interpolator expects power levels [W]; consumption/generation are
+                # energy per slot [Wh], so convert via the slot duration (identical at
+                # the hourly default, ×4 on the 15-minute grid).
                 scr = self.self_consumption_predictor.calculate_self_consumption(
-                    consumption, generation
+                    consumption / self.slot_duration_h, generation / self.slot_duration_h
                 )
 
                 # Remaining power after consumption
@@ -133,10 +136,8 @@ class Inverter:
 
                 if allow_battery_grid_export and self.battery:
                     export_capacity = max(self.max_power_wh - consumption - grid_export, 0.0)
-                    max_discharge_dc = getattr(self.battery, "max_charge_power_w", None)
+                    remaining_battery_ac = (
-                    if max_discharge_dc is not None:
+                        self.battery.remaining_discharge_energy_wh(hour) * dc_to_ac_eff
-                        remaining_battery_ac = max(
-                            (max_discharge_dc - from_battery_dc) * dc_to_ac_eff, 0.0
                     )
                     export_capacity = min(export_capacity, remaining_battery_ac)
                     battery_export_ac, battery_export_losses = self._discharge_battery_to_ac(
@@ -171,10 +172,8 @@ class Inverter:
 
             if allow_battery_grid_export and self.battery and grid_import <= 0.0:
                 export_capacity = max(self.max_power_wh - consumption, 0.0)
-                max_discharge_dc = getattr(self.battery, "max_charge_power_w", None)
+                remaining_battery_ac = (
-                if max_discharge_dc is not None:
+                    self.battery.remaining_discharge_energy_wh(hour) * dc_to_ac_eff
-                    remaining_battery_ac = max(
-                        (max_discharge_dc - battery_discharge_dc) * dc_to_ac_eff, 0.0
                 )
                 export_capacity = min(export_capacity, remaining_battery_ac)
                 battery_export_ac, battery_export_losses = self._discharge_battery_to_ac(

src/akkudoktoreos/optimization/genetic/genetic.py

@@ -100,9 +100,7 @@ class GeneticSimulation(PydanticBaseModel):
     )
     bat_grid_export_hours: Optional[NDArray[Shape["*"], float]] = Field(
         default=None,
-        json_schema_extra={
+        json_schema_extra={"description": "Hourly permission for battery discharge into the grid."},
-            "description": "Hourly permission for battery discharge into the grid."
-        },
     )
     ev_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(
         default=None, json_schema_extra={"description": "TBD"}
@@ -453,9 +451,7 @@ class GeneticSimulation(PydanticBaseModel):
 
             # Financial calculations
             costs_per_hour[hour_idx] = energy_consumption_grid_actual * hourly_electricity_price
-            revenue_per_hour[hour_idx] = (
+            revenue_per_hour[hour_idx] = energy_feedin_grid_actual * hourly_feed_in_tariff
-                energy_feedin_grid_actual * hourly_feed_in_tariff
-            )
 
         total_cost = np.nansum(costs_per_hour)
         total_losses = np.nansum(losses_wh_per_hour)
@@ -529,12 +525,23 @@ class GeneticOptimization(OptimizationBase):
         fixed_seed: Optional[int] = None,
     ):
         """Initialize the optimization problem with the required parameters."""
+        if self.config.optimization.interval not in (900, 3600):
+            logger.warning(
+                "Genetic optimization interval {} seconds is unsupported; using 3600 seconds.",
+                self.config.optimization.interval,
+            )
+            self.config.optimization.interval = 3600
         self.opti_param: dict[str, Any] = {}
         # Number of slots at the tail of the optimization window where EV
         # charging is fixed to 0. Slot-counted so 15-min runs reserve the right
         # tail length (at interval=3600 s this equals prediction.hours - horizon).
-        self.fixed_eauto_hours = self.total_slots - (
+        self.fixed_eauto_hours = max(
-            self.config.optimization.horizon_hours * self.slots_per_hour
+            self.total_slots
+            - (
+                self._start_day_slot()
+                + self.config.optimization.horizon_hours * self.slots_per_hour
+            ),
+            0,
         )
         self.ev_possible_charge_values: list[float] = [1.0]
         # Separate charge-level list for battery AC charging (independent of EV rates).
@@ -580,15 +587,111 @@ class GeneticOptimization(OptimizationBase):
             return parameters
 
         ems_parameters = parameters.ems.model_copy(
-            update={
+            update={"einspeiseverguetung_euro_pro_wh": list(parameters.ems.strompreis_euro_pro_wh)},
-                "einspeiseverguetung_euro_pro_wh": list(
-                    parameters.ems.strompreis_euro_pro_wh
-                )
-            },
             deep=True,
         )
         return parameters.model_copy(update={"ems": ems_parameters}, deep=True)
 
+    def _parameters_for_slot_grid(
+        self, parameters: GeneticOptimizationParameters
+    ) -> GeneticOptimizationParameters:
+        """Normalize hourly or native-slot EMS input onto the optimization grid.
+
+        API clients historically provide one value per prediction hour. At a
+        sub-hourly interval, energy quantities are distributed across the slots
+        while price quantities are held constant. Inputs already matching the
+        native slot grid are preserved exactly. Any other length is ambiguous and
+        rejected instead of silently shortening the simulation horizon.
+        """
+
+        def normalize(values: list[float], name: str, *, energy: bool) -> list[float]:
+            value_count = len(values)
+            if value_count == self.total_slots:
+                return list(values)
+            if value_count != self.config.prediction.hours:
+                raise ValueError(
+                    f"{name} has {value_count} values; expected either "
+                    f"{self.config.prediction.hours} hourly values or "
+                    f"{self.total_slots} optimization-slot values."
+                )
+
+            normalized = np.repeat(np.asarray(values, dtype=float), self.slots_per_hour)
+            if energy:
+                normalized /= self.slots_per_hour
+            return normalized.tolist()
+
+        ems = parameters.ems
+        feed_in_tariff = ems.einspeiseverguetung_euro_pro_wh
+        if isinstance(feed_in_tariff, list):
+            normalized_feed_in_tariff: list[float] | float = normalize(
+                feed_in_tariff,
+                "einspeiseverguetung_euro_pro_wh",
+                energy=False,
+            )
+        else:
+            normalized_feed_in_tariff = [float(feed_in_tariff)] * self.total_slots
+
+        normalized_ems = ems.model_copy(
+            update={
+                "pv_prognose_wh": normalize(ems.pv_prognose_wh, "pv_prognose_wh", energy=True),
+                "gesamtlast": normalize(ems.gesamtlast, "gesamtlast", energy=True),
+                "strompreis_euro_pro_wh": normalize(
+                    ems.strompreis_euro_pro_wh,
+                    "strompreis_euro_pro_wh",
+                    energy=False,
+                ),
+                "einspeiseverguetung_euro_pro_wh": normalized_feed_in_tariff,
+            },
+            deep=True,
+        )
+        temperature_forecast = parameters.temperature_forecast
+        if temperature_forecast is not None:
+            if len(temperature_forecast) == self.config.prediction.hours:
+                temperature_forecast = [
+                    value for value in temperature_forecast for _ in range(self.slots_per_hour)
+                ]
+            elif len(temperature_forecast) != self.total_slots:
+                raise ValueError(
+                    f"temperature_forecast has {len(temperature_forecast)} values; expected "
+                    f"either {self.config.prediction.hours} hourly values or "
+                    f"{self.total_slots} optimization-slot values."
+                )
+        return parameters.model_copy(
+            update={"ems": normalized_ems, "temperature_forecast": temperature_forecast},
+            deep=True,
+        )
+
+    def _start_solution_for_slot_grid(
+        self, start_solution: list[float], *, has_appliance: bool
+    ) -> list[float]:
+        """Expand a legacy hourly genome to the configured slot grid when possible."""
+        expected_length = self.total_slots * (2 if self.optimize_ev else 1)
+        hourly_length = self.config.prediction.hours * (2 if self.optimize_ev else 1)
+        if has_appliance:
+            expected_length += 1
+            hourly_length += 1
+
+        if len(start_solution) == expected_length or self.slots_per_hour == 1:
+            return list(start_solution)
+        if len(start_solution) != hourly_length:
+            return list(start_solution)
+
+        battery_end = self.config.prediction.hours
+        migrated = np.repeat(start_solution[:battery_end], self.slots_per_hour).tolist()
+        if self.optimize_ev:
+            ev_end = battery_end + self.config.prediction.hours
+            migrated.extend(
+                np.repeat(start_solution[battery_end:ev_end], self.slots_per_hour).tolist()
+            )
+        if has_appliance:
+            migrated.append(start_solution[-1])
+        logger.info(
+            "Expanded hourly start_solution from {} to {} slot values.",
+            hourly_length,
+            expected_length,
+        )
+        return migrated
+
     def decode_charge_discharge(
         self, discharge_hours_bin: np.ndarray
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
@@ -813,8 +916,15 @@ class GeneticOptimization(OptimizationBase):
         self.toolbox.register("mate", tools.cxTwoPoint)
 
         # Mutation operator for battery charge/discharge states
+        # Keep the expected number of mutated genes per hour stable when the
+        # interval becomes finer (0.2 hourly -> 0.05 on a quarter-hour grid).
+        mutation_probability = 0.2 / self.slots_per_hour
         self.toolbox.register(
-            "mutate_charge_discharge", tools.mutUniformInt, low=0, up=total_states - 1, indpb=0.2
+            "mutate_charge_discharge",
+            tools.mutUniformInt,
+            low=0,
+            up=total_states - 1,
+            indpb=mutation_probability,
         )
 
         # Mutation operator for EV states (separate index space)
@@ -823,7 +933,7 @@ class GeneticOptimization(OptimizationBase):
             tools.mutUniformInt,
             low=0,
             up=len_ev - 1,
-            indpb=0.2,
+            indpb=mutation_probability,
         )
 
         # Mutation for household appliance
@@ -1106,8 +1216,10 @@ class GeneticOptimization(OptimizationBase):
 
                     if best_uncovered_price < break_even_price:
                         # AC charging at this hour is economically unjustified.
-                        # Penalty = excess cost per Wh × DC energy requested this hour.
+                        # Penalty = excess cost per Wh × DC energy requested this slot.
-                        dc_wh = bat.max_charge_power_w * ac_factor
+                        # max_charge_power_w is a power [W]; the energy movable in
+                        # one slot is power × slot_duration_h (¼ at 15 min).
+                        dc_wh = bat.max_charge_power_w * self.slot_duration_h * ac_factor
                         ac_wh = dc_wh / max(inv.ac_to_dc_efficiency, 1e-9)
                         excess_cost_per_wh = break_even_price - best_uncovered_price
                         gesamtbilanz += ac_wh * excess_cost_per_wh * ac_penalty_factor
@@ -1138,6 +1250,12 @@ class GeneticOptimization(OptimizationBase):
 
         @TODO: optimize() ngen default (200) is different from optimierung_ems() ngen default (400).
         """
+        # Re-seed at the actual optimization boundary. Setup and validation may
+        # consume random values elsewhere in a long-running process; a fixed seed
+        # must nevertheless produce the same population and result.
+        if self.fix_seed is not None:
+            random.seed(self.fix_seed)
+
         # Set the number of inviduals in a generation
         try:
             individuals = self.config.optimization.genetic.individuals
@@ -1157,14 +1275,16 @@ class GeneticOptimization(OptimizationBase):
         logger.debug("Start optimize: {}", start_solution)
 
         # Insert the start solution into the population if provided and compatible with the
-        # currently active genome layout. EV optimization adds one gene per prediction hour,
+        # currently active genome layout. EV optimization adds one gene per prediction slot,
         # so a cached solution from a previous run without EV optimization must not be reused.
         if start_solution is not None:
-            expected_length = self.config.prediction.hours
+            has_appliance = self.opti_param.get("home_appliance", 0) > 0
-            if self.optimize_ev:
+            expected_length = self.total_slots * (2 if self.optimize_ev else 1)
-                expected_length += self.config.prediction.hours
+            if has_appliance:
-            if self.opti_param.get("home_appliance", 0) > 0:
                 expected_length += 1
+            start_solution = self._start_solution_for_slot_grid(
+                start_solution, has_appliance=has_appliance
+            )
 
             if len(start_solution) == expected_length:
                 for _ in range(10):
@@ -1218,6 +1338,12 @@ class GeneticOptimization(OptimizationBase):
         """Perform EMS (Energy Management System) optimization and visualize results."""
         direct_marketing_enabled = self._direct_marketing_enabled()
         parameters = self._parameters_for_config(parameters)
+        parameters = self._parameters_for_slot_grid(parameters)
+        if self.slots_per_hour > 1 and parameters.dishwasher is not None:
+            raise ValueError(
+                "Home-appliance scheduling is not yet supported for sub-hourly "
+                "optimization intervals."
+            )
         self.optimize_dc_charge = direct_marketing_enabled
         self.optimize_battery_grid_export = direct_marketing_enabled
 
@@ -1399,7 +1525,10 @@ class GeneticOptimization(OptimizationBase):
         else:
             battery_grid_export = battery_grid_export.tolist()
 
-        # Visualize the results in PDF
+        # Visualize the results in PDF. Skippable via config — matplotlib PDF
+        # generation costs several seconds per run, which headless setups
+        # (API/Node-RED polling) never look at.
+        if getattr(self.config.optimization, "visualize_pdf", True):
             try:
                 from akkudoktoreos.utils.visualize import prepare_visualize
 
@@ -1418,7 +1547,7 @@ class GeneticOptimization(OptimizationBase):
                     "fixed_seed": self.fix_seed,
                 }
 
-            prepare_visualize(parameters, visualize, start_hour=start_hour)
+                prepare_visualize(parameters, visualize, start_hour=start_slot)
 
             except Exception as ex:
                 error_msg = f"Visualization failed: {ex}"

src/akkudoktoreos/optimization/genetic/geneticparams.py

@@ -227,7 +227,7 @@ class GeneticOptimizationParameters(
         # Add forecast and device data
         interval = to_duration(cls.config.optimization.interval)
         power_to_energy_per_interval_factor = cls.config.optimization.interval / 3600
-        parameter_start_datetime = ems.start_datetime.set(hour=0, second=0, microsecond=0)
+        parameter_start_datetime = ems.start_datetime.set(hour=0, minute=0, second=0, microsecond=0)
         parameter_end_datetime = parameter_start_datetime.add(hours=cls.config.prediction.hours)
         max_retries = 10
 
@@ -248,7 +248,11 @@ class GeneticOptimizationParameters(
                         start_datetime=parameter_start_datetime,
                         end_datetime=parameter_end_datetime,
                         interval=interval,
-                        fill_method="linear",
+                        # Forecast power values represent the mean of their source
+                        # period. Hold them over smaller optimization slots so
+                        # resampling preserves energy (especially hourly and
+                        # Solcast 30-minute forecasts).
+                        fill_method="ffill",
                     )
                     * power_to_energy_per_interval_factor
                 ).tolist()
@@ -585,8 +589,12 @@ class GeneticOptimizationParameters(
             # Home Appliances
             # ---------------
             if cls.config.devices.max_home_appliances is None:
-                logger.info("Number of home appliance devices not configured - defaulting to 1.")
+                default_home_appliances = 0 if cls.config.optimization.interval < 3600 else 1
-                cls.config.devices.max_home_appliances = 1
+                logger.info(
+                    "Number of home appliance devices not configured - defaulting to {}.",
+                    default_home_appliances,
+                )
+                cls.config.devices.max_home_appliances = default_home_appliances
             if cls.config.devices.max_home_appliances == 0:
                 home_appliance_params = None
             else:

src/akkudoktoreos/optimization/genetic/geneticsolution.py

@@ -177,7 +177,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
         default_factory=list,
         json_schema_extra={
             "description": "Array with battery-to-grid export values (1 for export discharge, 0 otherwise)."
-        }
+        },
     )
     eautocharge_hours_float: Optional[list[float]] = Field(json_schema_extra={"description": "TBD"})
     result: GeneticSimulationResult
@@ -331,7 +331,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
         Clamping rules:
           - AC charge factor: scaled down proportionally when the battery
             headroom (max_soc − current_soc) is smaller than what the
-            commanded factor would store in one hour.  Set to 0 when full.
+            commanded factor would store in one optimization slot.  Set to 0 when full.
           - DC charge factor (PV): zeroed when battery is at or above max SOC
             (the inverter curtails automatically, but this makes intent clear).
           - Discharge: blocked when SOC is at or below min SOC.
@@ -361,9 +361,14 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
                     if inv_list and inv_list[0].max_ac_charge_power_w is not None
                     else float(bat.max_charge_power_w)
                 )
-                max_dc_per_h_wh = effective_ac * max_ac_cp_w * ac_to_dc_eff * ch_eff
+                # Energy storable in one optimization slot, not per hour: scale the
-                if max_dc_per_h_wh > headroom_wh:
+                # power [W] by the slot duration (1.0 hourly, 0.25 at 15 min).
-                    effective_ac = effective_ac * (headroom_wh / max_dc_per_h_wh)
+                slot_duration_h = float(self.config.optimization.interval or 3600) / 3600.0
+                max_dc_per_slot_wh = (
+                    effective_ac * max_ac_cp_w * slot_duration_h * ac_to_dc_eff * ch_eff
+                )
+                if max_dc_per_slot_wh > headroom_wh:
+                    effective_ac = effective_ac * (headroom_wh / max_dc_per_slot_wh)
 
         # --- DC charge (PV): zero when battery is full ---
         effective_dc = dc_charge
@@ -619,13 +624,13 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
         for pred_key, pred_fill_method, pred_solution_key, pred_solution_factor in [
             (
                 "pvforecast_ac_power",
-                "linear",
+                "ffill",
                 "pvforecast_ac_energy_wh",
                 power_to_energy_per_interval_factor,
             ),
             (
                 "pvforecast_dc_power",
-                "linear",
+                "ffill",
                 "pvforecast_dc_energy_wh",
                 power_to_energy_per_interval_factor,
             ),
@@ -637,7 +642,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
             ),
             (
                 "feed_in_tariff_wh",
-                "linear",
+                "ffill",
                 "feed_in_tariff_amt_kwh",
                 1000.0,
             ),
@@ -649,19 +654,19 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
             ),
             (
                 "loadforecast_power_w",
-                "linear",
+                "ffill",
                 "loadforecast_energy_wh",
                 power_to_energy_per_interval_factor,
             ),
             (
                 "loadakkudoktor_std_power_w",
-                "linear",
+                "ffill",
                 "loadakkudoktor_std_energy_wh",
                 power_to_energy_per_interval_factor,
             ),
             (
                 "loadakkudoktor_mean_power_w",
-                "linear",
+                "ffill",
                 "loadakkudoktor_mean_energy_wh",
                 power_to_energy_per_interval_factor,
             ),

src/akkudoktoreos/optimization/optimization.py

@@ -89,6 +89,18 @@ class OptimizationCommonSettings(SettingsBaseModel):
         },
     )
 
+    visualize_pdf: bool = Field(
+        default=True,
+        json_schema_extra={
+            "description": (
+                "Generate the PDF visualization after each optimization run. "
+                "Disable for headless setups (e.g. Node-RED integration) to save "
+                "several seconds per run. Defaults to True."
+            ),
+            "examples": [True, False],
+        },
+    )
+
     genetic: GeneticCommonSettings = Field(
         default_factory=GeneticCommonSettings,
         json_schema_extra={

src/akkudoktoreos/prediction/elecpriceenergycharts.py

@@ -176,6 +176,19 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
 
         return series_data
 
+    @staticmethod
+    def _resolution_seconds(series: pd.Series) -> int:
+        """Infer the current native market interval from recent timestamps."""
+        if len(series) < 2:
+            return 3600
+        index = pd.DatetimeIndex(series.sort_index().index).drop_duplicates()
+        deltas = index.to_series().diff().dropna().dt.total_seconds()
+        deltas = deltas[deltas > 0].tail(96)
+        if deltas.empty:
+            return 3600
+        resolution = int(round(float(deltas.median())))
+        return resolution if resolution > 0 and 3600 % resolution == 0 else 3600
+
     def _cap_outliers(self, data: np.ndarray, sigma: int = 2) -> np.ndarray:
         mean = data.mean()
         std = data.std()
@@ -250,37 +263,64 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
                 f"No Update ElecPriceEnergyCharts is needed, last in history: {self.highest_orig_datetime}"
             )
 
-        # Generate history array for prediction
-        history = self.key_to_array(
-            key="elecprice_marketprice_wh",
-            end_datetime=self.highest_orig_datetime,
-            fill_method="linear",
-        )
-
-        amount_datasets = len(self.records)
         if not self.highest_orig_datetime:  # mypy fix
             error_msg = f"Highest original datetime not available: {self.highest_orig_datetime}"
             logger.error(error_msg)
             raise ValueError(error_msg)
 
-        # some of our data is already in the future, so we need to predict less. If we got less data we increase the prediction hours
+        raw_series = self.key_to_series(
-        needed_hours = int(
+            key="elecprice_marketprice_wh",
-            self.config.prediction.hours
+            end_datetime=to_datetime(self.highest_orig_datetime).add(seconds=1),
-            - ((self.highest_orig_datetime - self.ems_start_datetime).total_seconds() // 3600)
+        )
+        resolution_seconds = self._resolution_seconds(raw_series)
+        slots_per_hour = 3600 // resolution_seconds
+        history = self.key_to_array(
+            key="elecprice_marketprice_wh",
+            end_datetime=self.highest_orig_datetime,
+            interval=to_duration(f"{resolution_seconds} seconds"),
+            fill_method="linear",
         )
 
-        if needed_hours <= 0:
+        # some of our data is already in the future, so we need to predict less. If we got less data we increase the prediction hours
+        covered_slots = 0
+        if self.highest_orig_datetime >= self.ems_start_datetime:
+            covered_slots = (
+                int(
+                    (self.highest_orig_datetime - self.ems_start_datetime).total_seconds()
+                    // resolution_seconds
+                )
+                + 1
+            )
+        needed_slots = self.config.prediction.hours * slots_per_hour - covered_slots
+
+        if needed_slots <= 0:
             logger.warning(
-                f"No prediction needed. needed_hours={needed_hours}, hours={self.config.prediction.hours},highest_orig_datetime {self.highest_orig_datetime}, start_datetime {self.ems_start_datetime}"
+                "No prediction needed. needed_slots={}, hours={}, resolution_seconds={}, "
+                "highest_orig_datetime={}, start_datetime={}",
+                needed_slots,
+                self.config.prediction.hours,
+                resolution_seconds,
+                self.highest_orig_datetime,
+                self.ems_start_datetime,
             )  # this might keep data longer than self.ems_start_datetime + self.config.prediction.hours in the records
             return
 
-        if amount_datasets > 800:  # we do the full ets with seasons of 1 week
+        weekly_history_slots = 800 * slots_per_hour
-            prediction = self._predict_ets(history, seasonal_periods=168, hours=needed_hours)
+        daily_history_slots = 168 * slots_per_hour
-        elif amount_datasets > 168:  # not enough data to do seasons of 1 week, but enough for 1 day
+        if len(history) > weekly_history_slots:
-            prediction = self._predict_ets(history, seasonal_periods=24, hours=needed_hours)
+            prediction = self._predict_ets(
-        elif amount_datasets > 0:  # not enough data for ets, do median
+                history,
-            prediction = self._predict_median(history, hours=needed_hours)
+                seasonal_periods=168 * slots_per_hour,
+                hours=needed_slots,
+            )
+        elif len(history) > daily_history_slots:
+            prediction = self._predict_ets(
+                history,
+                seasonal_periods=24 * slots_per_hour,
+                hours=needed_slots,
+            )
+        elif len(history) > 0:
+            prediction = self._predict_median(history, hours=needed_slots)
         else:
             logger.error("No data available for prediction")
             raise ValueError("No data available")
@@ -289,7 +329,7 @@ class ElecPriceEnergyCharts(ElecPriceProvider):
         prediction_series = pd.Series(
             data=prediction,
             index=[
-                self.highest_orig_datetime + to_duration(f"{i + 1} hours")
+                self.highest_orig_datetime + to_duration(f"{(i + 1) * resolution_seconds} seconds")
                 for i in range(len(prediction))
             ],
         )

src/akkudoktoreos/prediction/elecpricetibber.py

@@ -34,7 +34,40 @@ query TibberPriceInfo {
             total
           }
         }
-        priceInfoRange(resolution: QUARTER_HOURLY, last: 960) {
+        priceInfoRange(resolution: QUARTER_HOURLY, last: 672) {
+          nodes {
+            startsAt
+            total
+          }
+        }
+      }
+    }
+  }
+}
+"""
+
+# Same query, but requesting priceInfo (and therefore its today/tomorrow
+# fields) at quarter-hourly resolution. Tibber defines ``resolution`` on
+# Subscription.priceInfo, not on the nested PriceInfo.today/tomorrow fields.
+# Tried first; on a GraphQL schema error from an older API the provider falls
+# back to TIBBER_PRICE_QUERY.
+TIBBER_PRICE_QUERY_QUARTER_HOURLY = """
+query TibberPriceInfo {
+  viewer {
+    homes {
+      id
+      currentSubscription {
+        priceInfo(resolution: QUARTER_HOURLY) {
+          today {
+            startsAt
+            total
+          }
+          tomorrow {
+            startsAt
+            total
+          }
+        }
+        priceInfoRange(resolution: QUARTER_HOURLY, last: 672) {
           nodes {
             startsAt
             total
@@ -185,9 +218,17 @@ class ElecPriceTibber(ElecPriceProvider):
         if not access_token:
             raise ValueError("Tibber access_token is required")
 
+        # Prefer quarter-hourly today/tomorrow prices; fall back to the hourly
+        # query when the Tibber API rejects the resolution argument. Tibber
+        # signals schema errors either as HTTP 400 or as HTTP 200 with an
+        # "errors" array, so both must route to the fallback (raise_for_status
+        # must NOT run before the fallback check).
+        response = None
+        queries = (TIBBER_PRICE_QUERY_QUARTER_HOURLY, TIBBER_PRICE_QUERY)
+        for attempt, query in enumerate(queries, start=1):
             response = requests.post(
                 TIBBER_GRAPHQL_URL,
-            json={"query": TIBBER_PRICE_QUERY},
+                json={"query": query},
                 headers={
                     "Authorization": f"Bearer {access_token}",
                     "Content-Type": "application/json",
@@ -195,7 +236,20 @@ class ElecPriceTibber(ElecPriceProvider):
                 timeout=30,
             )
             logger.debug(f"Response from Tibber GraphQL API: {response}")
+            if response.ok and b'"errors"' not in response.content:
+                break
+            if attempt < len(queries):
+                logger.info(
+                    "Tibber rejected the quarter-hourly priceInfo query "
+                    "(HTTP {}): {} - falling back to hourly today/tomorrow prices.",
+                    response.status_code,
+                    response.text[:300],
+                )
+            else:
+                # Final (hourly) attempt failed for real - surface the error.
                 response.raise_for_status()
+        if response is None:  # pragma: no cover - the query tuple is never empty
+            raise RuntimeError("No Tibber GraphQL query was attempted")
         tibber_data = self._validate_data(response.content)
         self.update_datetime = to_datetime(in_timezone=self.config.general.timezone)
         return tibber_data

src/akkudoktoreos/prediction/feedintariffenergycharts.py

@@ -85,15 +85,18 @@ class FeedInTariffEnergyCharts(FeedInTariffProvider):
             series_data.at[orig_datetime] = price_eur_per_mwh / 1_000_000
         return series_data
 
-    def _predict_prices(self, history, hours: int):
+    def _predict_prices(self, history, slots: int, slots_per_hour: int):
         energycharts = ElecPriceEnergyCharts()
-        amount_datasets = len(self.records)
+        if len(history) > 800 * slots_per_hour:
-        if amount_datasets > 800:
+            return energycharts._predict_ets(
-            return energycharts._predict_ets(history, seasonal_periods=168, hours=hours)
+                history, seasonal_periods=168 * slots_per_hour, hours=slots
-        if amount_datasets > 168:
+            )
-            return energycharts._predict_ets(history, seasonal_periods=24, hours=hours)
+        if len(history) > 168 * slots_per_hour:
-        if amount_datasets > 0:
+            return energycharts._predict_ets(
-            return energycharts._predict_median(history, hours=hours)
+                history, seasonal_periods=24 * slots_per_hour, hours=slots
+            )
+        if len(history) > 0:
+            return energycharts._predict_median(history, hours=slots)
         logger.error("No feed-in tariff data available for Energy-Charts prediction")
         raise ValueError("No data available")
 
@@ -141,38 +144,52 @@ class FeedInTariffEnergyCharts(FeedInTariffProvider):
                 self.highest_orig_datetime,
             )
 
-        history = self.key_to_array(
-            key="feed_in_tariff_wh",
-            end_datetime=self.highest_orig_datetime,
-            fill_method="linear",
-        )
-
         if not self.highest_orig_datetime:
             error_msg = f"Highest original datetime not available: {self.highest_orig_datetime}"
             logger.error(error_msg)
             raise ValueError(error_msg)
 
-        needed_hours = int(
+        raw_series = self.key_to_series(
-            self.config.prediction.hours
+            key="feed_in_tariff_wh",
-            - ((self.highest_orig_datetime - self.ems_start_datetime).total_seconds() // 3600)
+            end_datetime=to_datetime(self.highest_orig_datetime).add(seconds=1),
+        )
+        resolution_seconds = ElecPriceEnergyCharts._resolution_seconds(raw_series)
+        slots_per_hour = 3600 // resolution_seconds
+        history = self.key_to_array(
+            key="feed_in_tariff_wh",
+            end_datetime=self.highest_orig_datetime,
+            interval=to_duration(f"{resolution_seconds} seconds"),
+            fill_method="linear",
         )
 
-        if needed_hours <= 0:
+        covered_slots = 0
+        if self.highest_orig_datetime >= self.ems_start_datetime:
+            covered_slots = (
+                int(
+                    (self.highest_orig_datetime - self.ems_start_datetime).total_seconds()
+                    // resolution_seconds
+                )
+                + 1
+            )
+        needed_slots = self.config.prediction.hours * slots_per_hour - covered_slots
+
+        if needed_slots <= 0:
             logger.warning(
-                "No feed-in tariff prediction needed. needed_hours={}, hours={}, "
+                "No feed-in tariff prediction needed. needed_slots={}, hours={}, "
-                "highest_orig_datetime={}, start_datetime={}",
+                "resolution_seconds={}, highest_orig_datetime={}, start_datetime={}",
-                needed_hours,
+                needed_slots,
                 self.config.prediction.hours,
+                resolution_seconds,
                 self.highest_orig_datetime,
                 self.ems_start_datetime,
             )
             return
 
-        prediction = self._predict_prices(history, needed_hours)
+        prediction = self._predict_prices(history, needed_slots, slots_per_hour)
         prediction_series = pd.Series(
             data=prediction,
             index=[
-                self.highest_orig_datetime + to_duration(f"{i + 1} hours")
+                self.highest_orig_datetime + to_duration(f"{(i + 1) * resolution_seconds} seconds")
                 for i in range(len(prediction))
             ],
         )

src/akkudoktoreos/prediction/interpolator.py

@@ -15,31 +15,44 @@ class SelfConsumptionProbabilityInterpolator:
         # Load the RegularGridInterpolator
         with open(self.filepath, "rb") as file:
             self.interpolator: RegularGridInterpolator = pickle.load(file)  # noqa: S301
+        self.load_power_min_w = float(self.interpolator.grid[0][0])
+        self.load_power_max_w = float(self.interpolator.grid[0][-1])
+        self.minute_load_max_w = float(self.interpolator.grid[1][-1])
 
     def _generate_points(
-        self, load_1h_power: float, pv_power: float
+        self, mean_load_power_w: float, pv_power_w: float
     ) -> tuple[np.ndarray, np.ndarray]:
-        """Generate the grid points for interpolation."""
+        """Generate in-bounds grid points for interpolation.
-        partial_loads = np.arange(0, pv_power + 50, 50)
+
-        points = np.array([np.full_like(partial_loads, load_1h_power), partial_loads]).T
+        The bundled probability table was calibrated from a one-hour mean load
+        and one-minute samples. Sub-hourly optimization still passes *power* in
+        watts here; a native 15-minute mean is therefore a documented
+        approximation until a separately calibrated table is available.
+        """
+        bounded_mean_load_w = float(
+            np.clip(mean_load_power_w, self.load_power_min_w, self.load_power_max_w)
+        )
+        bounded_pv_power_w = float(np.clip(pv_power_w, 0.0, self.minute_load_max_w))
+        partial_loads = np.arange(0.0, bounded_pv_power_w + 1.0, 50.0)
+        points = np.column_stack((np.full(partial_loads.shape, bounded_mean_load_w), partial_loads))
         return points, partial_loads
 
     @cache_energy_management
-    def calculate_self_consumption(self, load_1h_power: float, pv_power: float) -> float:
+    def calculate_self_consumption(self, mean_load_power_w: float, pv_power_w: float) -> float:
         """Calculate the PV self-consumption rate using RegularGridInterpolator.
 
         The results are cached until the start of the next energy management run/ optimization.
 
         Args:
-         - last_1h_power: 1h power levels (W).
+         - mean_load_power_w: Mean load power for the current forecast interval (W).
-         - pv_power: Current PV power output (W).
+         - pv_power_w: Current PV power output (W).
 
         Returns:
          - Self-consumption rate as a float.
         """
-        points, partial_loads = self._generate_points(load_1h_power, pv_power)
+        points, _ = self._generate_points(mean_load_power_w, pv_power_w)
         probabilities = self.interpolator(points)
-        return probabilities.sum()
+        return float(np.clip(probabilities.sum(), 0.0, 1.0))
 
     # def calculate_self_consumption(self, load_1h_power: float, pv_power: float) -> float:
     #     """Calculate the PV self-consumption rate using RegularGridInterpolator.

src/akkudoktoreos/prediction/prediction.py

@@ -82,7 +82,6 @@ elecprice_energy_charts = ElecPriceEnergyCharts()
 elecprice_tibber = ElecPriceTibber()
 elecprice_fixed = ElecPriceFixed()
 elecprice_import = ElecPriceImport()
-elecprice_tibber = ElecPriceTibber()
 feedintariff_energy_charts = FeedInTariffEnergyCharts()
 feedintariff_fixed = FeedInTariffFixed()
 feedintariff_import = FeedInTariffImport()
@@ -102,14 +101,14 @@ weather_openmeteo = WeatherOpenMeteo()
 weather_import = WeatherImport()
 
 
-def prediction_providers() -> list[
+def prediction_providers() -> (
+    list[
         Union[
             ElecPriceAkkudoktor,
             ElecPriceEnergyCharts,
             ElecPriceTibber,
             ElecPriceFixed,
             ElecPriceImport,
-        ElecPriceTibber,
             FeedInTariffEnergyCharts,
             FeedInTariffFixed,
             FeedInTariffImport,
@@ -128,7 +127,8 @@ def prediction_providers() -> list[
             WeatherOpenMeteo,
             WeatherImport,
         ]
-]:
+    ]
+):
     """Return list of prediction providers.
 
     Factory for prediction container.
@@ -139,7 +139,6 @@ def prediction_providers() -> list[
         elecprice_tibber, \
         elecprice_fixed, \
         elecprice_import, \
-        elecprice_tibber, \
         feedintariff_energy_charts, \
         feedintariff_fixed, \
         feedintariff_import, \
@@ -165,7 +164,6 @@ def prediction_providers() -> list[
         elecprice_tibber,
         elecprice_fixed,
         elecprice_import,
-        elecprice_tibber,
         feedintariff_energy_charts,
         feedintariff_fixed,
         feedintariff_import,
@@ -196,7 +194,6 @@ class Prediction(PredictionContainer):
             ElecPriceTibber,
             ElecPriceFixed,
             ElecPriceImport,
-            ElecPriceTibber,
             FeedInTariffEnergyCharts,
             FeedInTariffFixed,
             FeedInTariffImport,

src/akkudoktoreos/utils/visualize.py

@@ -149,9 +149,8 @@ class VisualizationReport(ConfigMixin):
         """Create a line chart and add it to the current group."""
 
         def chart() -> None:
-            timestamps = [
+            interval_s = int(self.config.optimization.interval or 3600)
-                start_date.add(hours=i) for i in range(len(y_list[0]))
+            timestamps = [start_date.add(seconds=i * interval_s) for i in range(len(y_list[0]))]
-            ]  # 840 timestamps at 1-hour intervals
 
             for idx, y_data in enumerate(y_list):
                 label = labels[idx] if labels else None  # Chart label
@@ -208,9 +207,10 @@ class VisualizationReport(ConfigMixin):
             # ax2.set_xticks(timestamps[::48])  # Set ticks every 12 hours
             # ax2.set_xticklabels([f"{int(h)}" for h in hours_since_start[::48]])
             # ax2.set_xticks(timestamps[:: len(timestamps) // 24])  # Select 10 evenly spaced ticks
-            ax2.set_xticks(timestamps[:: len(timestamps) // 12])  # Select 10 evenly spaced ticks
+            tick_step = max(1, len(timestamps) // 12)
+            ax2.set_xticks(timestamps[::tick_step])
             # ax2.set_xticklabels([f"{int(h)}" for h in hours_since_start[:: len(timestamps) // 24]])
-            ax2.set_xticklabels([f"{int(h)}" for h in hours_since_start[:: len(timestamps) // 12]])
+            ax2.set_xticklabels([f"{int(h)}" for h in hours_since_start[::tick_step]])
             if x2label:
                 ax2.set_xlabel(x2label)
 
@@ -443,12 +443,14 @@ def prepare_visualize(
     global debug_visualize
 
     report = VisualizationReport(filename)
-    next_full_hour_date = get_ems().start_datetime
+    start_datetime = get_ems().start_datetime
+    start_slot = start_hour  # Backwards-compatible argument name; value is a slot index.
+    interval_s = int(report.config.optimization.interval or 3600)
     # Group 1:
     report.create_line_chart_date(
-        next_full_hour_date,
+        start_datetime,
         [
-            parameters.ems.gesamtlast[start_hour:],
+            parameters.ems.gesamtlast[start_slot:],
         ],
         title="Load Profile",
         # xlabel="Hours", # not enough space
@@ -456,9 +458,9 @@ def prepare_visualize(
         labels=["Total Load (Wh)"],
     )
     report.create_line_chart_date(
-        next_full_hour_date,
+        start_datetime,
         [
-            parameters.ems.pv_prognose_wh[start_hour:],
+            parameters.ems.pv_prognose_wh[start_slot:],
         ],
         title="PV Forecast",
         # xlabel="Hours", # not enough space
@@ -466,11 +468,11 @@ def prepare_visualize(
     )
 
     report.create_line_chart_date(
-        next_full_hour_date,
+        start_datetime,
         [
             np.full(
-                len(parameters.ems.gesamtlast) - start_hour,
+                len(parameters.ems.gesamtlast) - start_slot,
-                parameters.ems.einspeiseverguetung_euro_pro_wh[start_hour:]
+                parameters.ems.einspeiseverguetung_euro_pro_wh[start_slot:]
                 if isinstance(parameters.ems.einspeiseverguetung_euro_pro_wh, list)
                 else parameters.ems.einspeiseverguetung_euro_pro_wh,
             )
@@ -482,9 +484,9 @@ def prepare_visualize(
     )
     if parameters.temperature_forecast:
         report.create_line_chart_date(
-            next_full_hour_date,
+            start_datetime,
             [
-                parameters.temperature_forecast[start_hour:],
+                parameters.temperature_forecast[start_slot:],
             ],
             title="Temperature Forecast",
             # xlabel="Hours", # not enough space
@@ -495,7 +497,7 @@ def prepare_visualize(
 
     # Group 2:
     report.create_line_chart_date(
-        next_full_hour_date,  # start_date
+        start_datetime,
         [
             results["result"]["Last_Wh_pro_Stunde"],
             results["result"]["Home_appliance_wh_per_hour"],
@@ -503,7 +505,7 @@ def prepare_visualize(
             results["result"]["Netzbezug_Wh_pro_Stunde"],
             results["result"]["Verluste_Pro_Stunde"],
         ],
-        title="Energy Flow per Hour",
+        title="Energy Flow per Interval",
         # xlabel="Date", # not enough space
         ylabel="Energy (Wh)",
         labels=[
@@ -520,7 +522,7 @@ def prepare_visualize(
 
     # Group 3:
     report.create_line_chart_date(
-        next_full_hour_date,  # start_date
+        start_datetime,
         [results["result"]["akku_soc_pro_stunde"], results["result"]["EAuto_SoC_pro_Stunde"]],
         title="Battery SOC",
         # xlabel="Date", # not enough space
@@ -532,27 +534,22 @@ def prepare_visualize(
         markers=["o", "x"],
     )
     report.create_line_chart_date(
-        next_full_hour_date,  # start_date
+        start_datetime,
-        [parameters.ems.strompreis_euro_pro_wh[start_hour:]],
+        [parameters.ems.strompreis_euro_pro_wh[start_slot:]],
         # title="Electricity Price", # not enough space
         # xlabel="Date", # not enough space
         ylabel="Electricity Price (€/Wh)",
         x2label=None,  # not enough space
     )
 
-    labels = list(
-        item
-        for sublist in zip(
-            list(str(i) for i in range(0, 23, 2)), list(str(" ") for i in range(0, 23, 2))
-        )
-        for item in sublist
-    )
-    labels = labels[start_hour:] + labels
-
     charge_discharge_series = [
-        results["ac_charge"][start_hour:],
+        results["ac_charge"][start_slot:],
-        results["dc_charge"][start_hour:],
+        results["dc_charge"][start_slot:],
-        results["discharge_allowed"][start_hour:],
+        results["discharge_allowed"][start_slot:],
+    ]
+    labels = [
+        start_datetime.add(seconds=i * interval_s).format("HH:mm")
+        for i in range(len(charge_discharge_series[0]))
     ]
     charge_discharge_labels = [
         "AC Charging (relative)",
@@ -561,7 +558,7 @@ def prepare_visualize(
     ]
     charge_discharge_colors = ["blue", "green", "red"]
     if results.get("battery_grid_export_allowed"):
-        charge_discharge_series.append(results["battery_grid_export_allowed"][start_hour:])
+        charge_discharge_series.append(results["battery_grid_export_allowed"][start_slot:])
         charge_discharge_labels.append("Battery Grid Export Allowed")
         charge_discharge_colors.append("purple")
 
@@ -580,12 +577,12 @@ def prepare_visualize(
     # Group 4:
 
     report.create_line_chart_date(
-        next_full_hour_date,  # start_date
+        start_datetime,
         [
             results["result"]["Kosten_Euro_pro_Stunde"],
             results["result"]["Einnahmen_Euro_pro_Stunde"],
         ],
-        title="Financial Balance per Hour",
+        title="Financial Balance per Interval",
         # xlabel="Date", # not enough space
         ylabel="Euro",
         labels=["Costs", "Revenue"],

tests/test_battery.py

@@ -294,3 +294,47 @@ def test_car_and_pv_battery_discharge_and_max_charge_power(setup_pv_battery, set
     assert car_battery.parameters.max_charge_power_w == 7000, (
         "Car battery max charge power should remain as defined"
     )
+
+
+def test_quarter_hour_charge_calls_share_one_power_budget():
+    params = SolarPanelBatteryParameters(
+        device_id="battery1",
+        capacity_wh=10_000,
+        initial_soc_percentage=0,
+        min_soc_percentage=0,
+        max_soc_percentage=100,
+        max_charge_power_w=1_000,
+        charging_efficiency=1.0,
+        discharging_efficiency=1.0,
+    )
+    battery = Battery(params, prediction_hours=4, slot_duration_h=0.25)
+    battery.set_charge_per_hour(np.ones(4))
+
+    first_stored, _ = battery.charge_energy(200.0, 0)
+    second_stored, _ = battery.charge_energy(200.0, 0)
+
+    assert first_stored == pytest.approx(200.0)
+    assert second_stored == pytest.approx(50.0)
+    assert battery.soc_wh == pytest.approx(250.0)
+
+
+def test_quarter_hour_discharge_calls_share_one_power_budget():
+    params = SolarPanelBatteryParameters(
+        device_id="battery1",
+        capacity_wh=10_000,
+        initial_soc_percentage=100,
+        min_soc_percentage=0,
+        max_soc_percentage=100,
+        max_charge_power_w=1_000,
+        charging_efficiency=1.0,
+        discharging_efficiency=1.0,
+    )
+    battery = Battery(params, prediction_hours=4, slot_duration_h=0.25)
+    battery.set_discharge_per_hour(np.ones(4))
+
+    first_delivered, _ = battery.discharge_energy(200.0, 0)
+    second_delivered, _ = battery.discharge_energy(200.0, 0)
+
+    assert first_delivered == pytest.approx(200.0)
+    assert second_delivered == pytest.approx(50.0)
+    assert battery.soc_wh == pytest.approx(9_750.0)

tests/test_elecpriceenergycharts.py

@@ -118,9 +118,9 @@ def test_update_data(mock_get, provider, sample_energycharts_json, cache_store):
 
     # Assert: Verify the result is as expected
     mock_get.assert_called_once()
-    assert (
+    assert len(provider) == 72
-        len(provider) == 73
+    # The final raw timestamp already represents its complete interval. Thus the
-    )  # we have 48 datasets in the api response, we want to know 48h into the future. The data we get has already 23h into the future so we need only 25h more. 48+25=73
+    # 48 API values need 24, rather than 25, additional hourly forecasts.
 
     # Assert we get hours prioce values by resampling
     np_price_array = provider.key_to_array(
@@ -131,10 +131,43 @@ def test_update_data(mock_get, provider, sample_energycharts_json, cache_store):
     assert len(np_price_array) == provider.total_hours
 
 
+def test_update_data_keeps_quarter_hour_resolution(provider):
+    # Use a range that does not overlap the hourly fixture data used by the
+    # neighbouring tests; the provider is a singleton by design.
+    start = to_datetime("2025-01-15 00:00:00", in_timezone="Europe/Berlin")
+    get_ems().set_start_datetime(start)
+    provider.highest_orig_datetime = None
+    raw_slots = provider.config.prediction.hours * 2
+    energy_charts_data = EnergyChartsElecPrice(
+        license_info="",
+        unix_seconds=[int(start.add(minutes=15 * i).timestamp()) for i in range(raw_slots)],
+        price=[100.0] * raw_slots,
+        unit="EUR/MWh",
+        deprecated=False,
+    )
+
+    with patch.object(provider, "_request_forecast", return_value=energy_charts_data):
+        provider._update_data(force_update=True)
+
+    result = provider.key_to_series(
+        key="elecprice_marketprice_wh",
+        start_datetime=start,
+        end_datetime=start.add(hours=provider.config.prediction.hours),
+    )
+    assert len(result) == provider.config.prediction.hours * 4
+    assert result.index.to_series().diff().dropna().dt.total_seconds().unique().tolist() == [900.0]
+
+
 @patch("requests.get")
 def test_update_data_with_incomplete_forecast(mock_get, provider):
     """Test `_update_data` with incomplete or missing forecast data."""
-    incomplete_data: dict = {"license_info": "", "unix_seconds": [], "price": [], "unit": "", "deprecated": False}
+    incomplete_data: dict = {
+        "license_info": "",
+        "unix_seconds": [],
+        "price": [],
+        "unit": "",
+        "deprecated": False,
+    }
     mock_response = Mock()
     mock_response.status_code = 200
     mock_response.content = json.dumps(incomplete_data)
@@ -218,6 +251,7 @@ def test_request_forecast_url_bidding_zone_is_value(mock_get, provider, sample_e
 
     # Extract the bzn= query parameter value from the URL
     from urllib.parse import parse_qs, urlparse
+
     parsed = urlparse(actual_url)
     query_params = parse_qs(parsed.query)
 

tests/test_elecpricetibber.py

@@ -12,6 +12,7 @@ from akkudoktoreos.prediction.elecprice import ElecPriceCommonSettings
 from akkudoktoreos.prediction.elecpricetibber import (
     ElecPriceTibber,
     ElecPriceTibberCommonSettings,
+    TIBBER_PRICE_QUERY_QUARTER_HOURLY,
     TibberGraphQLResponse,
 )
 from akkudoktoreos.utils.datetimeutil import to_datetime
@@ -255,6 +256,16 @@ def test_request_forecast_uses_tibber_graphql_api(
     assert kwargs["timeout"] == 30
 
 
+def test_quarter_hour_query_sets_resolution_on_price_info():
+    """Tibber defines resolution on priceInfo, not on today or tomorrow."""
+    compact_query = " ".join(TIBBER_PRICE_QUERY_QUARTER_HOURLY.split())
+
+    assert "priceInfo(resolution: QUARTER_HOURLY)" in compact_query
+    assert "today(resolution:" not in compact_query
+    assert "tomorrow(resolution:" not in compact_query
+    assert "priceInfoRange(resolution: QUARTER_HOURLY, last: 672)" in compact_query
+
+
 def test_tibber_update_extrapolates_missing_hours_with_seasonal_history(
     tibber_provider, monkeypatch
 ):

tests/test_feedintariffenergycharts.py

@@ -75,3 +75,27 @@ def test_request_forecast_uses_feedintariff_bidding_zone(
 
     actual_url = mock_get.call_args[0][0]
     assert "bzn=AT" in actual_url
+
+
+def test_update_data_keeps_quarter_hour_resolution(provider):
+    start = to_datetime("2025-01-15 00:00:00", in_timezone="Europe/Berlin")
+    get_ems().set_start_datetime(start)
+    raw_slots = provider.config.prediction.hours * 2
+    energy_charts_data = EnergyChartsElecPrice(
+        license_info="",
+        unix_seconds=[int(start.add(minutes=15 * i).timestamp()) for i in range(raw_slots)],
+        price=[100.0] * raw_slots,
+        unit="EUR/MWh",
+        deprecated=False,
+    )
+
+    with patch.object(provider, "_request_forecast", return_value=energy_charts_data):
+        provider._update_data(force_update=True)
+
+    result = provider.key_to_series(
+        key="feed_in_tariff_wh",
+        start_datetime=start,
+        end_datetime=start.add(hours=provider.config.prediction.hours),
+    )
+    assert len(result) == provider.config.prediction.hours * 4
+    assert result.index.to_series().diff().dropna().dt.total_seconds().unique().tolist() == [900.0]

tests/test_geneticsimulation.py

@@ -48,7 +48,9 @@ def genetic_simulation(config_eos) -> GeneticSimulation:
     akku.reset()
 
     inverter = Inverter(
-        InverterParameters(device_id="inverter1", max_power_wh=10000, battery_id=akku.parameters.device_id),
+        InverterParameters(
+            device_id="inverter1", max_power_wh=10000, battery_id=akku.parameters.device_id
+        ),
         battery=akku,
     )
 
@@ -301,69 +303,67 @@ def test_simulation(genetic_simulation):
     assert GeneticSimulationResult(**result) is not None
 
     # Check the length of the main arrays
-    assert len(result["Last_Wh_pro_Stunde"]) == 47, (
+    assert (
-        "The length of 'Last_Wh_pro_Stunde' should be 48."
+        len(result["Last_Wh_pro_Stunde"]) == 47
-    )
+    ), "The length of 'Last_Wh_pro_Stunde' should be 48."
-    assert len(result["Netzeinspeisung_Wh_pro_Stunde"]) == 47, (
+    assert (
-        "The length of 'Netzeinspeisung_Wh_pro_Stunde' should be 48."
+        len(result["Netzeinspeisung_Wh_pro_Stunde"]) == 47
-    )
+    ), "The length of 'Netzeinspeisung_Wh_pro_Stunde' should be 48."
-    assert len(result["Netzbezug_Wh_pro_Stunde"]) == 47, (
+    assert (
-        "The length of 'Netzbezug_Wh_pro_Stunde' should be 48."
+        len(result["Netzbezug_Wh_pro_Stunde"]) == 47
-    )
+    ), "The length of 'Netzbezug_Wh_pro_Stunde' should be 48."
-    assert len(result["Kosten_Euro_pro_Stunde"]) == 47, (
+    assert (
-        "The length of 'Kosten_Euro_pro_Stunde' should be 48."
+        len(result["Kosten_Euro_pro_Stunde"]) == 47
-    )
+    ), "The length of 'Kosten_Euro_pro_Stunde' should be 48."
-    assert len(result["akku_soc_pro_stunde"]) == 47, (
+    assert (
-        "The length of 'akku_soc_pro_stunde' should be 48."
+        len(result["akku_soc_pro_stunde"]) == 47
-    )
+    ), "The length of 'akku_soc_pro_stunde' should be 48."
 
     # Verify specific values in the 'Last_Wh_pro_Stunde' array
-    assert result["Last_Wh_pro_Stunde"][1] == 1527.13, (
+    assert (
-        "The value at index 1 of 'Last_Wh_pro_Stunde' should be 1527.13."
+        result["Last_Wh_pro_Stunde"][1] == 1527.13
-    )
+    ), "The value at index 1 of 'Last_Wh_pro_Stunde' should be 1527.13."
-    assert result["Last_Wh_pro_Stunde"][2] == 1468.88, (
+    assert (
-        "The value at index 2 of 'Last_Wh_pro_Stunde' should be 1468.88."
+        result["Last_Wh_pro_Stunde"][2] == 1468.88
-    )
+    ), "The value at index 2 of 'Last_Wh_pro_Stunde' should be 1468.88."
-    assert result["Last_Wh_pro_Stunde"][12] == 1132.03, (
+    assert (
-        "The value at index 12 of 'Last_Wh_pro_Stunde' should be 1132.03."
+        result["Last_Wh_pro_Stunde"][12] == 1132.03
-    )
+    ), "The value at index 12 of 'Last_Wh_pro_Stunde' should be 1132.03."
 
     # Verify that the value at index 0 is 'None'
     # Check that 'Netzeinspeisung_Wh_pro_Stunde' and 'Netzbezug_Wh_pro_Stunde' are consistent
-    assert result["Netzbezug_Wh_pro_Stunde"][1] == 1527.13, (
+    assert (
-        "The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 1527.13."
+        result["Netzbezug_Wh_pro_Stunde"][1] == 1527.13
-    )
+    ), "The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 1527.13."
 
     # Verify the total balance
-    assert abs(result["Gesamtbilanz_Euro"] - 6.612835813556755) < 1e-5, (
+    assert (
-        "Total balance should be 6.612835813556755."
+        abs(result["Gesamtbilanz_Euro"] - 6.62818441758576) < 1e-5
-    )
+    ), "Total balance should reflect the shared per-slot battery power limit."
 
     # Check total revenue and total costs
-    assert abs(result["Gesamteinnahmen_Euro"] - 1.964301131937134) < 1e-5, (
+    assert (
-        "Total revenue should be 1.964301131937134."
+        abs(result["Gesamteinnahmen_Euro"] - 1.9606946615517515) < 1e-5
-    )
+    ), "Total revenue should respect the shared per-slot battery power limit."
-    assert abs(result["Gesamtkosten_Euro"] - 8.577136945493889) < 1e-5, (
+    assert (
-        "Total costs should be 8.577136945493889 ."
+        abs(result["Gesamtkosten_Euro"] - 8.588879079137512) < 1e-5
-    )
+    ), "Total costs should respect the shared per-slot battery power limit."
 
     # Check the losses
-    assert abs(result["Gesamt_Verluste"] - 1620.0) < 1e-5, (
+    assert abs(result["Gesamt_Verluste"] - 1620.0) < 1e-5, "Total losses should be 1620.0 ."
-        "Total losses should be 1620.0 ."
-    )
 
     # Check the values in 'akku_soc_pro_stunde'
-    assert result["akku_soc_pro_stunde"][-1] == 98.0, (
+    assert (
-        "The value at index -1 of 'akku_soc_pro_stunde' should be 98.0."
+        result["akku_soc_pro_stunde"][-1] == 98.0
-    )
+    ), "The value at index -1 of 'akku_soc_pro_stunde' should be 98.0."
-    assert result["akku_soc_pro_stunde"][1] == 98.0, (
+    assert (
-        "The value at index 1 of 'akku_soc_pro_stunde' should be 98.0."
+        result["akku_soc_pro_stunde"][1] == 98.0
-    )
+    ), "The value at index 1 of 'akku_soc_pro_stunde' should be 98.0."
 
     # Check home appliances
-    assert sum(simulation.home_appliance.get_load_curve()) == 2000, (
+    assert (
-        "The sum of 'simulation.home_appliance.get_load_curve()' should be 2000."
+        sum(simulation.home_appliance.get_load_curve()) == 2000
-    )
+    ), "The sum of 'simulation.home_appliance.get_load_curve()' should be 2000."
 
     assert (
         np.nansum(
@@ -379,13 +379,17 @@ def test_simulation(genetic_simulation):
     print("All tests passed successfully.")
 
 
-def test_direct_marketing_curtails_negative_feed_in(config_eos):
+def test_direct_marketing_curtails_negative_feed_in(config_eos, monkeypatch):
     config_eos.merge_settings_from_dict(
         {"prediction": {"hours": 2}, "optimization": {"horizon_hours": 2}}
     )
 
     inverter = Inverter(InverterParameters(device_id="inverter1", max_power_wh=1000.0))
-    inverter.self_consumption_predictor.calculate_self_consumption = Mock(return_value=1.0)
+    monkeypatch.setattr(
+        inverter.self_consumption_predictor,
+        "calculate_self_consumption",
+        Mock(return_value=1.0),
+    )
 
     simulation = GeneticSimulation()
     simulation.prepare(

tests/test_interpolator.py

@@ -0,0 +1,30 @@
+import pytest
+
+from akkudoktoreos.prediction.interpolator import get_eos_load_interpolator
+
+
+def test_quarter_hour_energy_is_converted_back_to_same_mean_power():
+    """Splitting hourly energy must not change the minute-load probability lookup."""
+    interpolator = get_eos_load_interpolator()
+    hourly_load_wh = 800.0
+    hourly_pv_wh = 1200.0
+    slot_duration_h = 0.25
+
+    hourly = interpolator.calculate_self_consumption(hourly_load_wh, hourly_pv_wh)
+    quarter_hour = interpolator.calculate_self_consumption(
+        (hourly_load_wh / 4) / slot_duration_h,
+        (hourly_pv_wh / 4) / slot_duration_h,
+    )
+
+    assert quarter_hour == pytest.approx(hourly)
+
+
+def test_load_above_probability_grid_uses_highest_supported_distribution():
+    """Out-of-range household load must not make self-consumption jump to zero."""
+    interpolator = get_eos_load_interpolator()
+
+    at_boundary = interpolator.calculate_self_consumption(3450.0, 5000.0)
+    above_boundary = interpolator.calculate_self_consumption(4000.0, 5000.0)
+
+    assert above_boundary == pytest.approx(at_boundary)
+    assert above_boundary > 0.99

tests/test_inverter.py

@@ -1,8 +1,11 @@
 from unittest.mock import Mock, call, patch
 
+import numpy as np
 import pytest
 
+from akkudoktoreos.devices.genetic.battery import Battery
 from akkudoktoreos.devices.genetic.inverter import Inverter, InverterParameters
+from akkudoktoreos.optimization.genetic.geneticdevices import SolarPanelBatteryParameters
 
 
 @pytest.fixture
@@ -26,11 +29,51 @@ def inverter(mock_battery) -> Inverter:
             InverterParameters(
                 device_id="iv1", max_power_wh=500.0, battery_id=mock_battery.parameters.device_id
             ),
-            battery = mock_battery
+            battery=mock_battery,
         )
         return iv
 
 
+def test_quarter_hour_load_and_grid_export_share_discharge_power_limit():
+    """Local supply plus direct export may not exceed one slot's battery budget."""
+    battery = Battery(
+        SolarPanelBatteryParameters(
+            device_id="battery",
+            capacity_wh=10000,
+            charging_efficiency=1.0,
+            discharging_efficiency=1.0,
+            max_charge_power_w=7000,
+            initial_soc_percentage=100,
+        ),
+        prediction_hours=1,
+        slot_duration_h=0.25,
+    )
+    battery.set_discharge_per_hour(np.array([1]))
+    quarter_hour_inverter = Inverter(
+        InverterParameters(
+            device_id="inverter",
+            max_power_wh=10000,
+            battery_id="battery",
+            dc_to_ac_efficiency=1.0,
+            ac_to_dc_efficiency=1.0,
+        ),
+        battery=battery,
+        slot_duration_h=0.25,
+    )
+    initial_soc_wh = battery.soc_wh
+
+    grid_export, grid_import, _, _ = quarter_hour_inverter.process_energy(
+        generation=0.0,
+        consumption=1000.0,
+        hour=0,
+        allow_battery_grid_export=True,
+    )
+
+    assert grid_import == 0.0
+    assert grid_export == pytest.approx(750.0)
+    assert initial_soc_wh - battery.soc_wh == pytest.approx(1750.0)
+
+
 def test_process_energy_excess_generation(inverter, mock_battery):
     # Battery charges 100 Wh with 10 Wh loss
     mock_battery.charge_energy.return_value = (100.0, 10.0)
@@ -125,6 +168,7 @@ def test_process_energy_battery_discharges(inverter, mock_battery):
 
 def test_process_energy_allows_battery_grid_export(inverter, mock_battery):
     mock_battery.max_charge_power_w = 300.0
+    mock_battery.remaining_discharge_energy_wh.return_value = 200.0
     mock_battery.discharge_energy.side_effect = [(100.0, 0.0), (200.0, 0.0)]
 
     grid_export, grid_import, losses, self_consumption = inverter.process_energy(

tests/test_optimization_interval.py

@@ -16,6 +16,7 @@ from akkudoktoreos.config.config import ConfigEOS
 from akkudoktoreos.core.cache import CacheEnergyManagementStore
 from akkudoktoreos.core.coreabc import get_ems
 from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
+from akkudoktoreos.optimization.genetic.geneticdevices import HomeApplianceParameters
 from akkudoktoreos.optimization.genetic.geneticparams import (
     GeneticOptimizationParameters,
 )
@@ -27,6 +28,12 @@ ems_eos = get_ems(init=True)  # init once
 DIR_TESTDATA = Path(__file__).parent / "testdata"
 
 
+def load_hourly_parameters() -> GeneticOptimizationParameters:
+    """Load the legacy 48-value API example used by hourly clients."""
+    with (DIR_TESTDATA / "optimize_input_1.json").open("r") as f_in:
+        return GeneticOptimizationParameters(**json.load(f_in))
+
+
 @pytest.mark.parametrize(
     "interval, exp_slots_per_hour, exp_slot_duration_h",
     [
@@ -76,6 +83,189 @@ def test_start_day_slot_includes_minute_offset(config_eos: ConfigEOS):
     assert opt._start_day_slot() == sd.hour * 4 + sd.minute // 15
 
 
+def test_ems_start_is_floored_to_quarter_hour(config_eos: ConfigEOS):
+    """Rolling optimization starts at the current slot, not the previous full hour."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 900},
+        }
+    )
+
+    aligned = ems_eos.set_start_datetime(to_datetime().set(hour=10, minute=38, second=42))
+
+    assert aligned.hour == 10
+    assert aligned.minute == 30
+    assert aligned.second == 0
+
+
+def test_unsupported_interval_falls_back_to_hourly(config_eos: ConfigEOS):
+    """The genetic optimizer falls back without restricting interval-aware providers."""
+    config_eos.merge_settings_from_dict({"optimization": {"interval": 1800}})
+
+    assert config_eos.optimization.interval == 1800
+    GeneticOptimization(fixed_seed=42)
+    assert config_eos.optimization.interval == 3600
+
+
+def test_hourly_api_input_is_normalized_to_quarter_hour_slots(config_eos: ConfigEOS):
+    """Legacy API energy is split while prices are held over four slots."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 900},
+        }
+    )
+    parameters = load_hourly_parameters()
+    opt = GeneticOptimization(fixed_seed=42)
+
+    normalized = opt._parameters_for_slot_grid(parameters)
+
+    assert len(normalized.ems.pv_prognose_wh) == 192
+    assert len(normalized.ems.gesamtlast) == 192
+    assert len(normalized.ems.strompreis_euro_pro_wh) == 192
+    assert len(normalized.ems.einspeiseverguetung_euro_pro_wh) == 192
+    assert sum(normalized.ems.pv_prognose_wh[:4]) == pytest.approx(parameters.ems.pv_prognose_wh[0])
+    assert sum(normalized.ems.gesamtlast[:4]) == pytest.approx(parameters.ems.gesamtlast[0])
+    assert (
+        normalized.ems.strompreis_euro_pro_wh[:4] == [parameters.ems.strompreis_euro_pro_wh[0]] * 4
+    )
+    assert (
+        normalized.ems.einspeiseverguetung_euro_pro_wh[:4]
+        == [parameters.ems.einspeiseverguetung_euro_pro_wh[0]] * 4
+    )
+
+
+def test_native_quarter_hour_input_is_not_resampled(config_eos: ConfigEOS):
+    """Native 192-value input survives normalization without repetition or scaling."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 900},
+        }
+    )
+    parameters = load_hourly_parameters()
+    native_values = [float(i) for i in range(192)]
+    native_ems = parameters.ems.model_copy(
+        update={
+            "pv_prognose_wh": native_values,
+            "gesamtlast": native_values,
+            "strompreis_euro_pro_wh": native_values,
+            "einspeiseverguetung_euro_pro_wh": native_values,
+        },
+        deep=True,
+    )
+    native_parameters = parameters.model_copy(update={"ems": native_ems}, deep=True)
+
+    normalized = GeneticOptimization(fixed_seed=42)._parameters_for_slot_grid(native_parameters)
+
+    assert normalized.ems.pv_prognose_wh == native_values
+    assert normalized.ems.gesamtlast == native_values
+    assert normalized.ems.strompreis_euro_pro_wh == native_values
+    assert normalized.ems.einspeiseverguetung_euro_pro_wh == native_values
+
+
+def test_scalar_feed_in_tariff_fills_quarter_hour_grid(config_eos: ConfigEOS):
+    """A fixed feed-in tariff becomes one value per optimization slot."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 900},
+        }
+    )
+    parameters = load_hourly_parameters()
+    fixed_tariff = 0.00008
+    scalar_ems = parameters.ems.model_copy(
+        update={"einspeiseverguetung_euro_pro_wh": fixed_tariff}, deep=True
+    )
+    scalar_parameters = parameters.model_copy(update={"ems": scalar_ems}, deep=True)
+
+    normalized = GeneticOptimization(fixed_seed=42)._parameters_for_slot_grid(scalar_parameters)
+
+    assert normalized.ems.einspeiseverguetung_euro_pro_wh == [fixed_tariff] * 192
+
+
+def test_ambiguous_input_length_is_rejected(config_eos: ConfigEOS):
+    """Unexpected input lengths fail instead of silently shortening the simulation."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 900},
+        }
+    )
+    parameters = load_hourly_parameters()
+    invalid_ems = parameters.ems.model_copy(
+        update={
+            "pv_prognose_wh": [0.0] * 96,
+            "gesamtlast": [0.0] * 96,
+            "strompreis_euro_pro_wh": [0.0] * 96,
+            "einspeiseverguetung_euro_pro_wh": [0.0] * 96,
+        },
+        deep=True,
+    )
+    invalid_parameters = parameters.model_copy(update={"ems": invalid_ems}, deep=True)
+
+    with pytest.raises(ValueError, match="expected either 48 hourly values or 192"):
+        GeneticOptimization(fixed_seed=42)._parameters_for_slot_grid(invalid_parameters)
+
+
+def test_hourly_start_solution_is_expanded_to_slots(config_eos: ConfigEOS):
+    """A cached hourly genome becomes a valid quarter-hour warm start."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 900},
+        }
+    )
+    opt = GeneticOptimization(fixed_seed=42)
+    opt.optimize_ev = False
+    hourly = list(range(48))
+
+    migrated = opt._start_solution_for_slot_grid(hourly, has_appliance=False)
+
+    assert len(migrated) == 192
+    assert migrated[:8] == [0, 0, 0, 0, 1, 1, 1, 1]
+
+
+def test_quarter_hour_mutation_probability_preserves_hourly_rate(config_eos: ConfigEOS):
+    """A finer genome does not mutate four times as many controls per hour."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 900},
+        }
+    )
+    opt = GeneticOptimization(fixed_seed=42)
+    opt.optimize_ev = False
+    opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
+
+    assert opt.toolbox.mutate_charge_discharge.keywords["indpb"] == pytest.approx(0.05)
+
+
+def test_sub_hourly_home_appliance_is_rejected(config_eos: ConfigEOS):
+    """An hourly appliance model must not silently run on slot indices."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 900},
+        }
+    )
+    parameters = load_hourly_parameters().model_copy(
+        update={
+            "dishwasher": HomeApplianceParameters(
+                device_id="dishwasher", consumption_wh=1200, duration_h=2
+            )
+        },
+        deep=True,
+    )
+    ems_eos.set_start_datetime(to_datetime().set(hour=10, minute=0))
+
+    with pytest.raises(ValueError, match="Home-appliance scheduling"):
+        GeneticOptimization(fixed_seed=42).optimierung_ems(
+            parameters=parameters, start_hour=10, ngen=1
+        )
+
+
 def test_optimize_15min_slot_grid(config_eos: ConfigEOS):
     """An end-to-end optimization at interval=900 runs on a 192-slot day grid.
 
@@ -110,8 +300,7 @@ def test_optimize_15min_slot_grid(config_eos: ConfigEOS):
         }
     )
 
-    with (DIR_TESTDATA / "optimize_input_1.json").open("r") as f_in:
+    input_data = load_hourly_parameters()
-        input_data = GeneticOptimizationParameters(**json.load(f_in))
 
     ems_eos.set_start_datetime(to_datetime().set(hour=10, minute=0))
     CacheEnergyManagementStore().clear()
@@ -127,14 +316,15 @@ def test_optimize_15min_slot_grid(config_eos: ConfigEOS):
             parameters, results, filename=visualize_filename, **kwargs
         ),
     ):
-        genetic_solution = opt.optimierung_ems(
+        genetic_solution = opt.optimierung_ems(parameters=input_data, start_hour=10, ngen=3)
-            parameters=input_data, start_hour=10, ngen=3
-        )
 
     # The genetic core emitted a full-day grid at 15-min resolution.
     assert len(genetic_solution.ac_charge) == 192
     assert len(genetic_solution.dc_charge) == 192
     assert len(genetic_solution.discharge_allowed) == 192
+    expected_result_slots = 192 - opt._start_day_slot()
+    assert len(genetic_solution.result.Last_Wh_pro_Stunde) == expected_result_slots
+    assert len(genetic_solution.result.Electricity_price) == expected_result_slots
 
     # The serializers consume the 15-min grid without error and emit a 900 s
     # spaced solution index.

tests/test_prediction.py

@@ -7,6 +7,7 @@ from akkudoktoreos.prediction.elecpriceenergycharts import ElecPriceEnergyCharts
 from akkudoktoreos.prediction.elecpricefixed import ElecPriceFixed
 from akkudoktoreos.prediction.elecpriceimport import ElecPriceImport
 from akkudoktoreos.prediction.elecpricetibber import ElecPriceTibber
+from akkudoktoreos.prediction.feedintariffenergycharts import FeedInTariffEnergyCharts
 from akkudoktoreos.prediction.feedintarifffixed import FeedInTariffFixed
 from akkudoktoreos.prediction.feedintariffimport import FeedInTariffImport
 from akkudoktoreos.prediction.loadakkudoktor import (
@@ -46,6 +47,7 @@ def forecast_providers():
         ElecPriceTibber(),
         ElecPriceFixed(),
         ElecPriceImport(),
+        FeedInTariffEnergyCharts(),
         FeedInTariffFixed(),
         FeedInTariffImport(),
         LoadAkkudoktor(),
@@ -99,28 +101,32 @@ def test_provider_sequence(prediction):
     assert isinstance(prediction.providers[2], ElecPriceTibber)
     assert isinstance(prediction.providers[3], ElecPriceFixed)
     assert isinstance(prediction.providers[4], ElecPriceImport)
-    assert isinstance(prediction.providers[5], FeedInTariffFixed)
+    assert isinstance(prediction.providers[5], FeedInTariffEnergyCharts)
-    assert isinstance(prediction.providers[6], FeedInTariffImport)
+    assert isinstance(prediction.providers[6], FeedInTariffFixed)
-    assert isinstance(prediction.providers[7], LoadAkkudoktor)
+    assert isinstance(prediction.providers[7], FeedInTariffImport)
-    assert isinstance(prediction.providers[8], LoadAkkudoktorAdjusted)
+    assert isinstance(prediction.providers[8], LoadAkkudoktor)
-    assert isinstance(prediction.providers[9], LoadVrm)
+    assert isinstance(prediction.providers[9], LoadAkkudoktorAdjusted)
-    assert isinstance(prediction.providers[10], LoadImport)
+    assert isinstance(prediction.providers[10], LoadVrm)
-    assert isinstance(prediction.providers[11], PVForecastAkkudoktor)
+    assert isinstance(prediction.providers[11], LoadImport)
-    assert isinstance(prediction.providers[12], PVForecastVrm)
+    assert isinstance(prediction.providers[12], PVForecastAkkudoktor)
-    assert isinstance(prediction.providers[13], PVForecastPVNode)
+    assert isinstance(prediction.providers[13], PVForecastVrm)
-    assert isinstance(prediction.providers[14], PVForecastForecastSolar)
+    assert isinstance(prediction.providers[14], PVForecastPVNode)
-    assert isinstance(prediction.providers[15], PVForecastSolcast)
+    assert isinstance(prediction.providers[15], PVForecastForecastSolar)
-    assert isinstance(prediction.providers[16], PVForecastImport)
+    assert isinstance(prediction.providers[16], PVForecastSolcast)
-    assert isinstance(prediction.providers[17], WeatherBrightSky)
+    assert isinstance(prediction.providers[17], PVForecastImport)
-    assert isinstance(prediction.providers[18], WeatherClearOutside)
+    assert isinstance(prediction.providers[18], WeatherBrightSky)
-    assert isinstance(prediction.providers[19], WeatherOpenMeteo)
+    assert isinstance(prediction.providers[19], WeatherClearOutside)
-    assert isinstance(prediction.providers[20], WeatherImport)
+    assert isinstance(prediction.providers[20], WeatherOpenMeteo)
+    assert isinstance(prediction.providers[21], WeatherImport)
 
 
 def test_provider_by_id(prediction, forecast_providers):
     """Test that provider_by_id method returns the correct provider."""
     for provider in forecast_providers:
-        assert prediction.provider_by_id(provider.provider_id()).provider_id() == provider.provider_id()
+        assert (
+            prediction.provider_by_id(provider.provider_id()).provider_id()
+            == provider.provider_id()
+        )
 
 
 def test_prediction_repr(prediction):

tests/testdata/optimize_result_1.json

@@ -14,10 +14,11 @@
         0.0,
         0.0,
         0.0,
+        1.0,
+        1.0,
         0.0,
-        0.0,
+        1.0,
-        0.0,
+        1.0,
-        0.0,
         0.0,
         1.0,
         0.0,
@@ -39,12 +40,11 @@
         0.0,
         0.0,
         0.0,
+        1.0,
+        1.0,
         0.0,
-        0.0,
+        1.0,
-        0.0,
+        1.0,
-        0.0,
-        0.0,
-        0.0,
         0.0,
         0.0,
         0.0
@@ -111,44 +111,45 @@
         0,
         0,
         1,
-        0,
-        1,
         1,
         1,
         0,
-        1,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
         0,
         1,
         0,
-        1,
         0,
         1,
         1,
         1,
         1,
         0,
-        1,
         0,
         0,
         1,
-        0,
-        1,
         1,
         1,
         1,
         1,
         0,
         1,
-        0,
-        1,
-        0,
         1,
         0,
         0,
         0,
         1,
+        0,
+        0,
+        1,
+        1,
         0
     ],
+    "battery_grid_export_allowed": [],
     "eautocharge_hours_float": null,
     "result": {
         "Last_Wh_pro_Stunde": [
@@ -156,7 +157,7 @@
             1063.91,
             1320.56,
             1132.03,
-            1163.67,
+            1308.5200000002487,
             1176.82,
             1216.22,
             1103.78,
@@ -237,10 +238,10 @@
             0.0,
             0.0,
             0.0,
-            0.19391086083906173,
+            0.20303854854278033,
-            0.18681973047764083,
+            0.1899652543898917,
-            0.12880892587597292,
+            0.12833851757957424,
-            0.02404700392596282,
+            0.04233866391809883,
             0.0,
             0.0,
             0.0,
@@ -261,20 +262,20 @@
             0.0,
             0.0,
             0.0,
-            0.12500582038027028,
+            0.16357655037574115,
-            0.14608958812480227,
+            0.14900060381217387,
-            0.09047346757070289,
+            0.08949503544160814,
-            0.010404817872487553,
+            0.010110585812541543,
             0.0,
             0.0,
             0.0,
             0.0,
             0.0
         ],
-        "Gesamt_Verluste": 2878.660271824896,
+        "Gesamt_Verluste": 2807.7292841655817,
-        "Gesamtbilanz_Euro": 1.053854835771092,
+        "Gesamtbilanz_Euro": 0.8879905947253857,
-        "Gesamteinnahmen_Euro": 0.9055602150669013,
+        "Gesamteinnahmen_Euro": 0.9758637598724098,
-        "Gesamtkosten_Euro": 1.9594150508379933,
+        "Gesamtkosten_Euro": 1.8638543545977955,
         "Home_appliance_wh_per_hour": [
             0.0,
             0.0,
@@ -316,82 +317,82 @@
             0.0
         ],
         "Kosten_Euro_pro_Stunde": [
-            0.0,
-            0.004569992000000018,
             0.0,
             0.0,
             0.0,
+            0.001880612819166666,
+            0.026623430000091274,
             4.482711108977355e-14,
-            0.0,
+            0.008763569215739961,
-            0.016809914659344942,
+            0.018335381563380656,
-            0.0,
+            0.06267084962493971,
             0.05480703000000003,
+            0.225316611,
             0.0,
-            0.291163892,
+            0.26650619799999997,
+            0.19588158,
             0.0,
             0.0,
             0.0,
             0.0,
-            0.22802125600000003,
-            0.0,
             0.182970359,
             0.162995926,
-            0.0,
+            0.16677339,
-            0.26411047,
             0.0,
             0.0,
             0.0,
             0.0,
             0.0,
-            0.010682755832597498,
+            0.02844719513362201,
             0.0,
-            0.0003442778967139274,
             0.0,
-            0.028137079449292023,
+            0.0004412281431084693,
+            0.008372170029774037,
+            0.03130999506792791,
             0.0,
             0.08231598,
             0.174597189,
-            0.293043269,
+            0.0,
             0.0,
             0.16484566
         ],
         "Netzbezug_Wh_pro_Stunde": [
-            0.0,
-            20.660000000000082,
             0.0,
             0.0,
             0.0,
+            10.008583390988111,
+            144.8500000004966,
             2.236881790906864e-10,
-            0.0,
+            39.870651572975255,
-            74.05248748610107,
+            80.77260600608219,
-            0.0,
+            209.11194402715952,
             171.54000000000008,
+            731.31,
             0.0,
-            980.68,
+            912.38,
+            704.61,
             0.0,
             0.0,
             0.0,
             0.0,
-            694.34,
-            0.0,
             556.31,
             488.89,
-            0.0,
+            506.91,
-            799.85,
             0.0,
             0.0,
             0.0,
             0.0,
             0.0,
-            56.853410498124,
+            135.91588692604878,
             0.0,
-            1.7179535764168035,
             0.0,
-            123.95189184710142,
+            2.201737241060226,
+            38.089945540373236,
+            137.92949369131236,
             0.0,
             257.64,
             566.69,
-            987.01,
+            0.0,
             0.0,
             592.97
         ],
@@ -401,10 +402,10 @@
             0.0,
             0.0,
             0.0,
-            2770.155154843739,
+            2900.550693468291,
-            2668.8532925377262,
+            2713.7893484270244,
-            1840.127512513899,
+            1833.4073939939178,
-            343.5286275137546,
+            604.8380559728405,
             0.0,
             0.0,
             0.0,
@@ -425,10 +426,10 @@
             0.0,
             0.0,
             0.0,
-            1785.797434003861,
+            2336.807862510588,
-            2086.994116068604,
+            2128.580054459627,
-            1292.4781081528986,
+            1278.5005063086878,
-            148.64025532125078,
+            144.4369401791649,
             0.0,
             0.0,
             0.0,
@@ -437,83 +438,83 @@
         ],
         "Verluste_Pro_Stunde": [
             16.744090909090914,
-            0.0,
+            2.817272727272737,
             29.157272727272726,
-            3.7179773678125034,
+            2.358169993081436,
-            582.6180000000041,
+            600.0000000000002,
-            188.65138141872444,
+            173.00391678377832,
-            10.782457846871594,
             0.0,
-            59.810111380126784,
             0.0,
-            99.72409090909093,
             0.0,
-            124.41545454545451,
+            0.0,
-            96.08318181818186,
+            0.0,
+            133.72909090909081,
+            0.0,
+            0.0,
             70.41409090909087,
             118.37045454545455,
-            0.0,
+            94.68272727272722,
             83.01681818181817,
             0.0,
             0.0,
-            69.12409090909085,
             0.0,
+            109.0704545454546,
             109.96227272727276,
             47.952272727272714,
-            16.01263877609336,
+            16.031648664443644,
-            55.946263193163475,
+            55.9754990365584,
-            161.62968357967037,
+            143.33449356887422,
-            14.209990740225123,
+            21.973794868109557,
             538.2984000000038,
-            227.42215349036655,
+            161.2428480298022,
-            10.13011689299087,
-            0.0,
-            44.377460775206174,
             0.0,
             0.0,
+            44.91187685729284,
             0.0,
+            0.0,
+            134.59227272727276,
             100.08954545454549,
             0.0
         ],
         "akku_soc_pro_stunde": [
             80.0,
             79.4714617768595,
-            79.4714617768595,
+            79.38253271349862,
-            78.55109331955923,
+            78.46216425619835,
-            78.57585051614844,
+            78.52766897822838,
-            94.75968384947988,
+            95.19433564489505,
-            100.0,
-            100.0,
-            100.0,
-            100.0,
-            100.0,
-            96.85214359504131,
-            96.85214359504131,
-            92.92488808539943,
-            89.89195936639118,
-            87.6692923553719,
-            83.93285123966942,
-            83.93285123966942,
-            81.31237086776858,
-            81.31237086776858,
-            81.31237086776858,
-            79.13042355371898,
-            79.13042355371898,
-            75.65939221763084,
-            74.14574724517905,
-            74.30696088041155,
-            74.82732877552169,
-            78.33526266026307,
-            78.72998462526934,
-            93.68271795860093,
             100.0,
             100.0,
             100.0,
             100.0,
             100.0,
             100.0,
+            95.77875344352617,
+            95.77875344352617,
+            95.77875344352617,
+            93.55608643250687,
+            89.8196453168044,
+            86.83092286501376,
+            84.21044249311292,
+            84.21044249311292,
+            84.21044249311292,
+            84.21044249311292,
+            80.76756198347105,
+            77.2965306473829,
+            75.78288567493111,
+            75.93522642877453,
+            76.44194846937079,
+            80.42346217961729,
+            80.56829866584056,
+            95.52103199917215,
             100.0,
-            96.84060778236915
+            100.0,
+            100.0,
+            100.0,
+            100.0,
+            100.0,
+            95.75150654269973,
+            92.59211432506888
         ],
         "Electricity_price": [
             0.000228,
@@ -554,6 +555,46 @@
             0.0002969,
             0.0002921,
             0.000278
+        ],
+        "Feed_in_tariff": [
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05,
+            7e-05
         ]
     },
     "eauto_obj": {
@@ -667,14 +708,12 @@
         "initial_soc_percentage": 54
     },
     "start_solution": [
-        1.0,
+        0.0,
-        1.0,
-        1.0,
-        1.0,
         0.0,
         1.0,
+        2.0,
         0.0,
-        1.0,
+        2.0,
         2.0,
         1.0,
         1.0,
@@ -683,90 +722,40 @@
         1.0,
         1.0,
         0.0,
-        1.0,
+        2.0,
+        2.0,
+        0.0,
+        2.0,
+        2.0,
         0.0,
-        1.0,
         2.0,
         1.0,
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tests/testdata/optimize_result_1_be.json

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tests/testdata/optimize_result_2.json

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 }