Data prefetch for ems (#418)

* Pre-fetch data

* maintanance and extend tests

* comment clean up

* nansum usage (to be save)

src/akkudoktoreos/core/ems.py

@@ -1,4 +1,4 @@
-from typing import Any, ClassVar, Dict, Optional, Union
+from typing import Any, ClassVar, Optional
 
 import numpy as np
 from numpydantic import NDArray, Shape
@@ -186,7 +186,7 @@ class EnergieManagementSystem(SingletonMixin, ConfigMixin, PredictionMixin, Pyda
                 len(self.load_energy_array), parameters.einspeiseverguetung_euro_pro_wh, float
             )
         )
-        if inverter is not None:
+        if inverter:
             self.battery = inverter.battery
         else:
             self.battery = None
@@ -198,7 +198,7 @@ class EnergieManagementSystem(SingletonMixin, ConfigMixin, PredictionMixin, Pyda
         self.ev_charge_hours = np.full(self.config.prediction_hours, 0.0)
 
     def set_akku_discharge_hours(self, ds: np.ndarray) -> None:
-        if self.battery is not None:
+        if self.battery:
             self.battery.set_discharge_per_hour(ds)
 
     def set_akku_ac_charge_hours(self, ds: np.ndarray) -> None:
@@ -211,7 +211,7 @@ class EnergieManagementSystem(SingletonMixin, ConfigMixin, PredictionMixin, Pyda
         self.ev_charge_hours = ds
 
     def set_home_appliance_start(self, ds: int, global_start_hour: int = 0) -> None:
-        if self.home_appliance is not None:
+        if self.home_appliance:
             self.home_appliance.set_starting_time(ds, global_start_hour=global_start_hour)
 
     def reset(self) -> None:
@@ -276,53 +276,50 @@ class EnergieManagementSystem(SingletonMixin, ConfigMixin, PredictionMixin, Pyda
         return self.simulate(start_hour)
 
     def simulate(self, start_hour: int) -> dict[str, Any]:
-        """hour.
+        """Simulate energy usage and costs for the given start hour.
 
         akku_soc_pro_stunde begin of the hour, initial hour state!
-        last_wh_pro_stunde integral of  last hour (end state)
+        last_wh_pro_stunde integral of last hour (end state)
         """
         # Check for simulation integrity
-        missing_data = []
-
-        if self.load_energy_array is None:
-            missing_data.append("Load Curve")
-        if self.pv_prediction_wh is None:
-            missing_data.append("PV Forecast")
-        if self.elect_price_hourly is None:
-            missing_data.append("Electricity Price")
-        if self.ev_charge_hours is None:
-            missing_data.append("EV Charge Hours")
-        if self.ac_charge_hours is None:
-            missing_data.append("AC Charge Hours")
-        if self.dc_charge_hours is None:
-            missing_data.append("DC Charge Hours")
-        if self.elect_revenue_per_hour_arr is None:
-            missing_data.append("Feed-in Tariff")
+        required_attrs = [
+            "load_energy_array",
+            "pv_prediction_wh",
+            "elect_price_hourly",
+            "ev_charge_hours",
+            "ac_charge_hours",
+            "dc_charge_hours",
+            "elect_revenue_per_hour_arr",
+        ]
+        missing_data = [
+            attr.replace("_", " ").title() for attr in required_attrs if getattr(self, attr) is None
+        ]
 
         if missing_data:
-            error_msg = "Mandatory data missing - " + ", ".join(missing_data)
-            logger.error(error_msg)
-            raise ValueError(error_msg)
-        else:
-            # make mypy happy
-            assert self.load_energy_array is not None
-            assert self.pv_prediction_wh is not None
-            assert self.elect_price_hourly is not None
-            assert self.ev_charge_hours is not None
-            assert self.ac_charge_hours is not None
-            assert self.dc_charge_hours is not None
-            assert self.elect_revenue_per_hour_arr is not None
+            logger.error("Mandatory data missing - %s", ", ".join(missing_data))
+            raise ValueError(f"Mandatory data missing: {', '.join(missing_data)}")
 
-        load_energy_array = self.load_energy_array
+        # Pre-fetch data
+        load_energy_array = np.array(self.load_energy_array)
+        pv_prediction_wh = np.array(self.pv_prediction_wh)
+        elect_price_hourly = np.array(self.elect_price_hourly)
+        ev_charge_hours = np.array(self.ev_charge_hours)
+        ac_charge_hours = np.array(self.ac_charge_hours)
+        dc_charge_hours = np.array(self.dc_charge_hours)
+        elect_revenue_per_hour_arr = np.array(self.elect_revenue_per_hour_arr)
 
-        if not (
-            len(load_energy_array) == len(self.pv_prediction_wh) == len(self.elect_price_hourly)
-        ):
-            error_msg = f"Array sizes do not match: Load Curve = {len(load_energy_array)}, PV Forecast = {len(self.pv_prediction_wh)}, Electricity Price = {len(self.elect_price_hourly)}"
+        # Fetch objects
+        battery = self.battery
+        assert battery  # to please mypy
+        ev = self.ev
+        home_appliance = self.home_appliance
+        inverter = self.inverter
+
+        if not (len(load_energy_array) == len(pv_prediction_wh) == len(elect_price_hourly)):
+            error_msg = f"Array sizes do not match: Load Curve = {len(load_energy_array)}, PV Forecast = {len(pv_prediction_wh)}, Electricity Price = {len(elect_price_hourly)}"
             logger.error(error_msg)
             raise ValueError(error_msg)
 
-        # Optimized total hours calculation
         end_hour = len(load_energy_array)
         total_hours = end_hour - start_hour
 
@@ -332,116 +329,110 @@ class EnergieManagementSystem(SingletonMixin, ConfigMixin, PredictionMixin, Pyda
         consumption_energy_per_hour = np.full((total_hours), np.nan)
         costs_per_hour = np.full((total_hours), np.nan)
         revenue_per_hour = np.full((total_hours), np.nan)
-        soc_per_hour = np.full((total_hours), np.nan)  # Hour End State
+        soc_per_hour = np.full((total_hours), np.nan)
         soc_ev_per_hour = np.full((total_hours), np.nan)
         losses_wh_per_hour = np.full((total_hours), np.nan)
         home_appliance_wh_per_hour = np.full((total_hours), np.nan)
         electricity_price_per_hour = np.full((total_hours), np.nan)
 
         # Set initial state
-        if self.battery:
-            soc_per_hour[0] = self.battery.current_soc_percentage()
-        if self.ev:
-            soc_ev_per_hour[0] = self.ev.current_soc_percentage()
+        soc_per_hour[0] = battery.current_soc_percentage()
+        if ev:
+            soc_ev_per_hour[0] = ev.current_soc_percentage()
 
         for hour in range(start_hour, end_hour):
-            hour_since_now = hour - start_hour
+            hour_idx = hour - start_hour
 
             # save begin states
-            if self.battery:
-                soc_per_hour[hour_since_now] = self.battery.current_soc_percentage()
-            else:
-                soc_per_hour[hour_since_now] = 0.0
-            if self.ev:
-                soc_ev_per_hour[hour_since_now] = self.ev.current_soc_percentage()
+            soc_per_hour[hour_idx] = battery.current_soc_percentage()
+
+            if ev:
+                soc_ev_per_hour[hour_idx] = ev.current_soc_percentage()
 
             # Accumulate loads and PV generation
-            consumption = self.load_energy_array[hour]
-            losses_wh_per_hour[hour_since_now] = 0.0
+            consumption = load_energy_array[hour]
+            losses_wh_per_hour[hour_idx] = 0.0
 
             # Home appliances
-            if self.home_appliance:
-                ha_load = self.home_appliance.get_load_for_hour(hour)
+            if home_appliance:
+                ha_load = home_appliance.get_load_for_hour(hour)
                 consumption += ha_load
-                home_appliance_wh_per_hour[hour_since_now] = ha_load
+                home_appliance_wh_per_hour[hour_idx] = ha_load
 
             # E-Auto handling
-            if self.ev:
-                if self.ev_charge_hours[hour] > 0:
-                    loaded_energy_ev, verluste_eauto = self.ev.charge_energy(
-                        None, hour, relative_power=self.ev_charge_hours[hour]
-                    )
-                    consumption += loaded_energy_ev
-                    losses_wh_per_hour[hour_since_now] += verluste_eauto
+            if ev and ev_charge_hours[hour] > 0:
+                loaded_energy_ev, verluste_eauto = ev.charge_energy(
+                    None, hour, relative_power=ev_charge_hours[hour]
+                )
+                consumption += loaded_energy_ev
+                losses_wh_per_hour[hour_idx] += verluste_eauto
 
             # Process inverter logic
-            energy_feedin_grid_actual, energy_consumption_grid_actual, losses, eigenverbrauch = (
-                0.0,
-                0.0,
-                0.0,
-                0.0,
+            energy_feedin_grid_actual = energy_consumption_grid_actual = losses = eigenverbrauch = (
+                0.0
             )
-            if self.battery:
-                self.battery.set_charge_allowed_for_hour(self.dc_charge_hours[hour], hour)
-            if self.inverter:
-                energy_produced = self.pv_prediction_wh[hour]
+
+            hour_ac_charge = ac_charge_hours[hour]
+            hour_dc_charge = dc_charge_hours[hour]
+            hourly_electricity_price = elect_price_hourly[hour]
+            hourly_energy_revenue = elect_revenue_per_hour_arr[hour]
+
+            battery.set_charge_allowed_for_hour(hour_dc_charge, hour)
+
+            if inverter:
+                energy_produced = pv_prediction_wh[hour]
                 (
                     energy_feedin_grid_actual,
                     energy_consumption_grid_actual,
                     losses,
                     eigenverbrauch,
-                ) = self.inverter.process_energy(energy_produced, consumption, hour)
+                ) = inverter.process_energy(energy_produced, consumption, hour)
 
             # AC PV Battery Charge
-            if self.battery and self.ac_charge_hours[hour] > 0.0:
-                self.battery.set_charge_allowed_for_hour(1, hour)
-                battery_charged_energy_actual, battery_losses_actual = self.battery.charge_energy(
-                    None, hour, relative_power=self.ac_charge_hours[hour]
+            if hour_ac_charge > 0.0:
+                battery.set_charge_allowed_for_hour(1, hour)
+                battery_charged_energy_actual, battery_losses_actual = battery.charge_energy(
+                    None, hour, relative_power=hour_ac_charge
                 )
-                # print(hour, " ", battery_charged_energy_actual, " ",self.ac_charge_hours[hour]," ",self.battery.current_soc_percentage())
-                consumption += battery_charged_energy_actual
-                consumption += battery_losses_actual
-                energy_consumption_grid_actual += battery_charged_energy_actual
-                energy_consumption_grid_actual += battery_losses_actual
-                losses_wh_per_hour[hour_since_now] += battery_losses_actual
 
-            feedin_energy_per_hour[hour_since_now] = energy_feedin_grid_actual
-            consumption_energy_per_hour[hour_since_now] = energy_consumption_grid_actual
-            losses_wh_per_hour[hour_since_now] += losses
-            loads_energy_per_hour[hour_since_now] = consumption
-            electricity_price_per_hour[hour_since_now] = self.elect_price_hourly[hour]
+                total_battery_energy = battery_charged_energy_actual + battery_losses_actual
+                consumption += total_battery_energy
+                energy_consumption_grid_actual += total_battery_energy
+                losses_wh_per_hour[hour_idx] += battery_losses_actual
+
+            # Update hourly arrays
+            feedin_energy_per_hour[hour_idx] = energy_feedin_grid_actual
+            consumption_energy_per_hour[hour_idx] = energy_consumption_grid_actual
+            losses_wh_per_hour[hour_idx] += losses
+            loads_energy_per_hour[hour_idx] = consumption
+            electricity_price_per_hour[hour_idx] = hourly_electricity_price
 
             # Financial calculations
-            costs_per_hour[hour_since_now] = (
-                energy_consumption_grid_actual * self.elect_price_hourly[hour]
-            )
-            revenue_per_hour[hour_since_now] = (
-                energy_feedin_grid_actual * self.elect_revenue_per_hour_arr[hour]
-            )
+            costs_per_hour[hour_idx] = energy_consumption_grid_actual * hourly_electricity_price
+            revenue_per_hour[hour_idx] = energy_feedin_grid_actual * hourly_energy_revenue
 
-        # Total cost and return
-        gesamtkosten_euro = np.nansum(costs_per_hour) - np.nansum(revenue_per_hour)
+        total_cost = np.nansum(costs_per_hour)
+        total_losses = np.nansum(losses_wh_per_hour)
+        total_revenue = np.nansum(revenue_per_hour)
 
         # Prepare output dictionary
-        out: Dict[str, Union[np.ndarray, float]] = {
+        return {
             "Last_Wh_pro_Stunde": loads_energy_per_hour,
             "Netzeinspeisung_Wh_pro_Stunde": feedin_energy_per_hour,
             "Netzbezug_Wh_pro_Stunde": consumption_energy_per_hour,
             "Kosten_Euro_pro_Stunde": costs_per_hour,
             "akku_soc_pro_stunde": soc_per_hour,
             "Einnahmen_Euro_pro_Stunde": revenue_per_hour,
-            "Gesamtbilanz_Euro": gesamtkosten_euro,
+            "Gesamtbilanz_Euro": total_cost - total_revenue,
             "EAuto_SoC_pro_Stunde": soc_ev_per_hour,
-            "Gesamteinnahmen_Euro": np.nansum(revenue_per_hour),
-            "Gesamtkosten_Euro": np.nansum(costs_per_hour),
+            "Gesamteinnahmen_Euro": total_revenue,
+            "Gesamtkosten_Euro": total_cost,
             "Verluste_Pro_Stunde": losses_wh_per_hour,
-            "Gesamt_Verluste": np.nansum(losses_wh_per_hour),
+            "Gesamt_Verluste": total_losses,
             "Home_appliance_wh_per_hour": home_appliance_wh_per_hour,
             "Electricity_price": electricity_price_per_hour,
         }
 
-        return out
-
 
 # Initialize the Energy Management System, it is a singleton.
 ems = EnergieManagementSystem()