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Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
  Bobby NoelteandClaude Opus 5.5
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faf37abfe7 |
feat(devices): add inverter profiles
An inverter profile describes the technical data of an inverter model: its nominal AC power, the DC→AC and AC→DC efficiency curves over the load fraction (AC power relative to the nominal power), the AC charge power limit of the model, whether that limit also limits PV charging, and an optional power derating over the ambient temperature. Curves are read linearly and clamped at their ends, so every lookup yields a value. The packaged profile generic-hybrid has typical hybrid inverter efficiencies, no nominal power (the inverter device has to provide it) and full power up to 45 degC. Inverter settings name their profile (inverter_profile, default generic-hybrid) and hold the differences of the unit in profile_adjustment: nominal_power_w (replaces the profile value, required for a generic profile), max_power_w and max_ac_charge_power_w (installation limits that can only lower; an AC charge limit of 0 disables AC charging). The effective parameters are resolved at the ambient temperature of the inverter, given by the new measurement key <device_id>-temperature-degc (45 degC without a fresh value); the power derating lowers the maximum power and the AC charge limit. Optimization parameters are always valid: the GENETIC and GENETIC0 parameter preparation resolve the inverter with resolve_current_operating_parameters_or_defaults, which never raises. An unusable profile gives the generic-hybrid profile, a missing nominal power the fallback of 10000 W, an inverter derated to zero power the parameters at 45 degC, and no usable profile at all the profile adjustment with constant efficiency; each is logged as error. The GENETIC inverter takes the AC→DC efficiency curve of the profile, solved exactly on each linear piece of the curve (about 2 % longer runs); GENETIC0 keeps constant efficiencies and no longer replaces the configured inverters with the demo inverter; a battery, electric vehicle or inverter that cannot be prepared is left out of the GENETIC0 run, never replaced by a demo device. Configurations migrate: max_power_w becomes profile_adjustment.nominal_power_w, max_ac_charge_power_w moves into the adjustment, an ac_to_dc_efficiency of 0 keeps AC charging disabled, and the efficiency fields are dropped with an error log, as for batteries, so the profile efficiencies apply. Home Assistant config paths follow. Additional changes - Move the curve algorithms of the battery profile into profilecurve, so battery and inverter profiles share them; the battery profile keeps its document format and classes. - Move ProfileBaseModel, the strict base of profile documents, into profilestore. - Serve inverter profiles through the profile REST API. Refuse to delete a profile that a configured device uses and that has no packaged profile to revert to (409). - Share the measurement access of the device settings in devicemeasurement. - Rename check_battery_profiles to check_device_profiles; the device profile check at configuration setup covers inverters, too. - Document inverter profiles, their configuration, defaults and sharing; regenerate the configuration and OpenAPI documentation. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Assisted-by: Claude Code (claude-opus-5-5) |
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Bobby NoelteandClaude Opus 5.5
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e255f987bf |
feat(devices): add battery profiles
A battery profile describes a battery model: the charge and discharge limit
maps over temperature and SoC, the charge and discharge efficiency curves
over C-rate, the technical SoC bounds, the hard temperature cutoffs and,
for a model, its nominal capacity and power limits. EOS packages the
generic profile generic-lfp; users add their own profiles.
A battery or electric vehicle selects its profile by battery_profile and
describes how the installation differs from it in profile_adjustment
(capacity, power limits of the installation, periodic full charge). The
effective values are resolved from both at the measured operating point
(temperature, SoC, power) and shown as effective_* fields.
The GENETIC simulation uses the limits and efficiencies of the profile over
SoC, with precomputed tables as a fast path; GENETIC0 gets the values at the
operating point. Optimization parameters are always valid: a battery whose
profile is missing, unreadable or invalid uses the packaged generic-lfp
profile, and the former defaults only if that is not usable either; each is
logged as error, and an optimization run is never cancelled because of a
battery.
Additional changes
- Add a generic device profile store: user and packaged profiles, atomic
saves, compiled profiles cached per file and checked again at most once a
second, plain file names as profile names ('schema' is reserved). On
Windows, a save or read that collides with the other is retried briefly.
- Add the REST API /v1/profile to list the profile kinds and to list, read
(document and JSON schema), save and delete profiles.
- Check the hard temperature cutoffs on the exact temperature.
- Resolve inhibited charging or discharging to a power of 0, e.g. for a cold
battery; give GENETIC0 its own discharge limit for that.
- Limit generic profiles without own power limits to the former 5000 W.
- Accept only the implemented units; current limits need the nominal
voltage.
- Migrate legacy battery settings into the profile adjustment, keep the
former charge power limit as discharge limit (also for a Home Assistant
entity on it) and report dropped efficiencies as errors.
- Limit GENETIC battery discharge by the discharge power.
- Allow battery profile lookups while the configuration is created.
- Resolve a battery once per configuration dump.
- Package the bundled profiles, document battery profiles and regenerate
the configuration and OpenAPI documentation.
- Ignore the report PDFs written by the visualization tests.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Assisted-by: Claude Code (claude-opus-5-5)
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62cbf4cf2a |
fix(homeassistant): apply 0 and off from config entities, never add settings
The configuration sync of the Home Assistant adapter skipped every value that is false in Python, so an entity state of 0 or off never reached the configuration. It also set any path it was given: the documented example devices/batteries/0/capacity_wh named a device "0" that does not exist, so EOS silently added a new battery with a random device id. Apply every value except an unavailable or unknown entity, and only update settings that exist; report other paths as an error. The example now addresses the battery by its device_id. Signed-off-by: SchaafAlexander <86607129+SchaafAlexander@users.noreply.github.com> Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> |
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  ChenYi-NnergyandGitHub
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38b07f5a5b |
fix: apply inverter_paco cap and system loss in PVForecastPVLib - #1504
Planes sharing one inverter_paco feed a single physical inverter, but their AC output was summed without any cap, so a 15 kW inverter could report 21-25 kW. This PR groups the per-plane AC output by inverter_paco and clips each group's sum to its rating; planes without a rating are left alone. It also applies the configured system loss (PVGIS-style percent, same semantics as PVForecastAkkudoktorLocal) to DC and AC output instead of warning that it is unsupported. |
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628226d3d9 |
feat(pvforecast): model snow on the modules in the local backend (#1484)
No PV forecast modelled snow lying on the modules, so after a snowfall the optimizer planned with PV that never came. The local Open-Meteo/pvlib backend can now reduce the forecast by the snow coverage (opt-in via pvforecast.akkudoktor.snow_enabled): - Request hourly snowfall and snow depth with the weather and run the NREL snow coverage model (pvlib.snow) per plane. - Correct the coverage with measured PV production: production above the model clears it at once; low production raises it only while snow is plausible, so curtailment and inverter outages are not learned as snow. Persisting snow damps the slide rate, fading with snow_feedback_memory_hours. - Rebuild the coverage from snow_lookback_days on every update instead of storing state. - Exclude intervals with possible snow from the calibration fit. - Warn when PV meter readings are missing or stale; ignore impossible meter jumps. _plane_power keeps its two-value signature (used by the adaptive provider in #1422); the irradiance comes from the new _plane_power_and_poa. Tests replay real February 2026 data from one plant (snow on 02-19/20). Signed-off-by: SchaafAlexander <86607129+SchaafAlexander@users.noreply.github.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> |
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a06473b6d6 |
feat(battery): plan a periodic full charge of the stationary battery (#1486)
LFP batteries can only recalibrate their state of charge close to full charge, so many systems expect a full charge every few weeks and some start one on their own at an arbitrary, possibly expensive time. EOS can now plan it instead (GENETIC only, off by default). New battery settings: full_charge_interval_days (unset = off), full_charge_soc_percentage, full_charge_detect_percentage, full_charge_allow_grid and the informational cell_chemistry. The last full charge is read from the measured SoC history; nothing is stored. When the next one is due within the horizon (or overdue), the maximum SoC is raised to the target. With full_charge_allow_grid the optimizer must reach the target once before the deadline, which is at least 24 h away (penalty battery_full_charge_miss on the best SoC reached), so it picks the cheapest time itself. Without it, PV surplus may fill the battery but the target is not enforced. Signed-off-by: SchaafAlexander <86607129+SchaafAlexander@users.noreply.github.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> |
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 NormannandGitHub
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b33953d445 |
fix(pvforecast): normalize Forecast.Solar power before resampling (#1487)
Forecast.Solar supplies instantaneous power with irregular sunrise/sunset timestamps. GENETIC interprets stored values as interval averages and infers cadence from the shortest gap, so these samples create artificial coverage gaps. The prediction list API also averages away the sunset zero before interpolating, producing positive nighttime PV. Integrate each roof plane's piecewise-linear power curve into UTC-contiguous 15-minute interval-average W before summing planes. Both hourly and quarter-hour GENETIC slots then consume the same normalized data as the prediction API, with exactly one downstream W-to-Wh conversion. Explicit sunrise/sunset zero boundaries establish the adjacent night within each forecast day. Daylight gaps longer than an hour, invalid power, absent planes and unavailable forecast days remain missing; the strict GENETIC coverage check stays in place. Refreshes replace the current forecast and remove stale tails. Because cadence detection also sees historical records, the first refresh clears legacy forecasts containing non-aligned timestamps, including their history. Later refreshes retain normalized history. This upgrade behavior is documented. Forecast validation errors now identify the first invalid interval and the number of invalid slots. |
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f804d48072 |
feat(pvforecast): add adaptive per-plane PV learning and diagnostics (#1422)
PV arrays with different orientations and curtailment need separate plant calibration and weather correction. This opt-in provider publishes normal EOS AC/DC forecast keys at 15-minute resolution. - Preserve daily per-plane energy/shape calibration and validated short-term local/trend selection. Curtailment-sensitive arrays require a fresh release flag or an explicitly configured learning guard. - Add switchable empirical solar-angle response relative to separate eligible reference arrays, removing their common weather error. This learns angular residuals, not physical angles or a measured geometric horizon. - Archive actually received weather model inputs, first local receipt and frozen per-member PV candidates. Returned past weather is excluded; unknown source issuance remains explicit. Learn model weights and forecast-regime bias separately at 2–6, 6–24 and 24–48 hours. - New long-horizon candidates remain in shadow until their genuinely issued outputs improve equal-day MAE by the configured margin, win on at least 60% of validation days and pass a matched-support energy-bias guard. Insufficient evidence retains calibration. Daily yields still require complete local days, including DST. - Expose fitted parameters, independent training/validation day counts, errors, selected methods and persistence in the existing status API, with additional normal prediction keys for Grafana. Retain physical DC through angular/weather corrections an enforce individual inverter AC limits. Co-authored-by: Normann <github@koldrack.com> Co-authored-by: Christin <chrissi@von-perbandt.de> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: YMT <yn276364@dal.ca> Co-authored-by: sarankumar1325 <120080287+sarankumar1325@users.noreply.github.com> |
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NormannandBobby Noelte
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d22628b312 | fix: honor effective time window boundaries in cycle scheduling | ||
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Bobby Noelte
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c02d250639 |
feat: Add month and end_time to time window configuration
Extend TimeWindow with two new optional fields: - month (1–12): restricts the window to a start date in the given calendar month. Useful for seasonal schedules. - end_time: explicit exclusive end-of-window clock time. The effective end is the later of end_time and start_time + duration. Primary motivation is DST handling — a wall-clock end_time stays correct across DST transitions, whereas a fixed duration does not. Rework window semantics so date, weekday, and month restrictions apply to the window's start date, not to every timestamp covered by the window. This makes overnight windows (e.g. Friday 22:00 → Saturday 06:00) behave intuitively under calendar constraints; contains() now checks both today's and yesterday's candidate start. duration becomes optional (default 0). All start/end/duration computations route through _window_start_end and a new _matches_start_date helper, so contains, earliest_start_time, latest_start_time, and available_duration stay consistent. Dashboard: add end_time and month inputs to the time-window editor, render the collapsed window summary as icons + pills. Docs, OpenAPI spec, and generated config docs are regenerated for the new fields. Tests cover month filtering, end_time boundary cases (incl. midnight and overnight), and start-date semantics for overnight windows. Bump version to support automatic updates in Home Assistant. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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AndreasandGitHub
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cab088f09b |
fix(planning): accept full SoC targets and clarify unlimited EV SoC age (#1407)
* fix(devices): allow charging targets equal to the maximum SoC * test(planning): protect EV SoC without age limits |
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AndreasandGitHub
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49ea062dc8 | fix(genetic): allow optimization without fresh SoC (#1403) | ||
 
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7f26ea3f3f |
feat(devices): add load-dependent DC-to-AC efficiency curve for GENETIC inverters (#1375)
* feat(devices): add load-dependent DC-to-AC efficiency curve for GENETIC inverters Real inverters are much less efficient at low load than at rated power (e.g. 0.80 at 2 % vs. 0.93 at 7 % load). A constant dc_to_ac_efficiency overrates night-time battery discharge into a small base load and, with it, arbitrage and grid charging. Add two optional inverter settings for the GENETIC optimizer: - dc_to_ac_efficiency_curve: list of (load_fraction, efficiency) points, load fraction = AC power / max_power_w. Points must be strictly increasing in [0, 1] with efficiencies in (0, 1]; at least two points. Linear interpolation, clamped at both ends. When set, the curve replaces dc_to_ac_efficiency. - dc_to_ac_efficiency_reference_load_fraction (default 0.06): load at which the curve values stored energy without a specific conversion. Battery discharges in Inverter.process_energy() use the efficiency at the AC energy of each conversion. The end-of-horizon value, the terminal and tail value curves and the AC charge break-even penalty use Inverter.reference_dc_to_ac_efficiency. Without a curve, dc_to_ac_efficiency_at() and the reference efficiency return dc_to_ac_efficiency itself, so results are bit-identical to the previous model. GENETIC0 is unchanged. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(devices): evaluate the efficiency curve at the delivered AC load When the battery limits a discharge (low state of charge or its power limit), the conversion ran with the efficiency of the requested AC energy. With a 1000 Wh battery at 10 % SoC, a 1000 W inverter and the curve [(0, .8), (.5, .95), (1, .95)], a request of 500 Wh AC returned 95 Wh AC from the available 100 Wh DC, although the curve gives 82.5 % at that load. The delivered AC energy now solves a = dc * efficiency(a / rated) exactly on the piecewise-linear curve (solve_ac_for_dc_energy), bounded by the request. Unlimited discharges and inverters without a curve are unchanged. Regression tests use a real Battery: low SoC and a power-limited discharge, each with 60- and 15-minute slots, battery discharge losses, the load path through process_energy, and constant efficiency. * docs: regenerate OpenAPI for the efficiency-curve fix --------- Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: Andreas <drbacke@gmx.de> |
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14d8587c9c |
feat(genetic): make the fitness cache size configurable (#1376)
The per-run fitness cache of the GENETIC optimizer is unbounded. It grows with individuals x generations (~110k keys at 300 x 400, about 1.1 kB each, 1.6 kB with an EV) and adds 120-170 MB to the peak memory of a run, while only 6-15 % of the lookups hit. On 512 MB boards this is the largest part of the optimizer's memory. Add optimization.genetic.fitness_cache_max_entries: - None (default): unbounded, the previous behaviour. - 0: no cache; every genome is evaluated. - N > 0: at most N keys. The cache is then an OrderedDict and evicts the oldest key first (FIFO, O(1)); LRU would cost bookkeeping on every hit for a cache that rarely hits. A cache hit returns exactly what the deterministic evaluation would return, so the plan for a fixed seed is identical with any setting. Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: Andreas <drbacke@gmx.de> |
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ecec39fcd8 |
docs: add a guide for running EOS on small devices (#1379)
Collect the settings that bring EOS from about 290 MB idle / 400-450 MB during a GENETIC run down to about 140 MB, with the measurements behind them: fitness cache, timezone override and SciPy-free self-consumption tables (#1376, #1377, #1378), autosave for SD/eMMC storage, and the glibc allocator and BLAS environment. Includes run times on a Raspberry Pi 4 and an x86 VM with data of a real installation. Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> |
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NormannandGitHub
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e203a81666 |
fix: surface DVhub feed-in tariff update failures (#1357)
* fix: surface DVhub feed-in tariff update failures * test: annotate DVhub sample records for mypy * fix: require real feed-in tariff coverage in GENETIC0 * test: satisfy GENETIC0 tariff coverage checks * test: patch DVhub storage method on its class |
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NormannandGitHub
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4fe879ddd2 |
fix: allow EOSdash through an external reverse proxy (#1355)
Support externally proxied EOSdash (#1320) * test: verify optional dashboard port stays unpublished * docs: describe optional proxy access and public URL |
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AndreasandGitHub
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f5c6d2bc1e |
docs: explain energy planner upgrade and compatibility changes (#1331)
* docs: explain energy planner upgrade and compatibility changes * docs: add upgrade guide to documentation navigation * docs: resolve changelog upgrade link in generated site |
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8224c64654 |
feat: integrate device and runtime configuration foundation on main (#1328)
* feat: adapt configuration for multi optimization algorithms Decouple configuration from optimization algorithm parameters. Add to_[algorithm]_param() methods to the configuration that derive optimization algorithm specific parameters from the configuration. Add x-scope tags to the configuration options that describe for which specific algorithms the configuration option is for. The whole device settings are restructured. There are now general settings for the device classes with the afore mentioned to_[algorithm]_param() methods. The general device settings got their own directory `devices/settings`. By this the parameter class also does not have to be a pydantic model which can be used for future optimization/ simulations speed up. Also the parameter class for a device is now part of the device module. This better decouples and also is the natural place for parameters of a device. Besides this feature there are also fixes and improvements: * feat: extend home appliance time window settings and simulation Home appliance can now be configured for multiple runs with per-cycle allowed time windows. The number of remaining cycles to plan is determined at runtime by reading the ``cycles_completed_measurement_key`` from the measurement store. * feat: specialiced CycleTimeWindowSequence for time window sequences Sequence of time windows associated to cycles. This model specializes ``ValueTimeWindowSequence`` so that the ``value`` field of each ``ValueTimeWindow`` encodes the **cycle index** (0-based integer) the window belongs to. Typical use: an appliance that must run ``n`` times per day, each run constrained to a distinct time window. Assign ``value=0`` to windows for the first cycle, ``value=1`` for the second, and so on. Multiple windows may share the same cycle index (their allowed regions are unioned). Windows with ``value=None`` are silently ignored by all cycle-aware methods. * fix: Make test_configmigrate also regard the _ANY_SENTENIEL in key values * chore: Make devices configurations a map instead of a list This makes config paths stable regardless of declaration order and lets each device settings class build its own config path from ``self.device_id`` without needing an external index. Tests are adapted likewise. Devices configurations are automatically migrated from lists to maps. * chore: rename levelized_cost_of_storage_kwh to levelized_cost_of_storage_amt kwh This better fits in the naming scheme and also makes clear the costs are money. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> * fix: runtime config update ignored by config file Runtime settings were handed back to pydantic-settings as init settings, which rank below the config file and the environment. Any key already present in EOS.config.json or in the environment silently discarded the update, so a bulk PUT /v1/config returned 200 without applying anything, while the granular PUT /v1/config/{path} endpoint kept working. Add a dedicated runtime settings source ranked directly below the command line arguments and record granular updates there as well, so both endpoints share one store that survives re-evaluation of the settings sources. Environment variables keep precedence over the config file for all keys that were not set at runtime. Also repairs revert_settings() and update(), which passed their data through the same init settings. Closes #1303 * fix: env vars ignored on first config build ConfigEOS.__init__ passed self as first positional argument to _setup, which forwards it to pydantic_settings.BaseSettings.__init__. Its first positional parameter is _case_sensitive, so the environment source matched the upper case variable names against the lower case field names and returned nothing. Environment settings only took effect after the next configuration setup. * docs: changelog for config priority fixes * fix(config): preserve device identities and storage costs during migration * fix(devices): preserve charge-rate typing and public import compatibility * ruff format fix * fix(config): satisfy typed device conversion and migration contracts * docs(config): refresh validated configuration prerequisite schemas --------- Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: r0b2g1t <r0b2g1t@users.noreply.github.com> |
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AndreasandAndreas
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6529c39df4 |
feat(pvforecast): add calibrated local Akkudoktor backend
Port local PV modeling and outage calibration from feature commits |
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Bobby NoelteandGitHub
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ba76087db9 |
feat: electricity fee provider framework and generic providers (#1235)
Add new provider class for electricity fees providers. Add the generic providers: - ElecFeeFixed - ElecFeeImport The providers provide predictions for: - elecfee_consumption_amt_wh: Total fixed fee for consumed energy per Wh [amount/Wh]. This is the accumulation of all fixed per-Wh fees payable on "consumed energy - such as network charge, concession fee, and electricity charge - into a single amount. - elecfee_consumption_percent_amt: Total fixed surcharge on consumed energy, given as a percentage of the monetary amount already charged for that energy [%]. This is the accumulation of all percentage-based surcharges payable on top of the consumed-energy fee - such as VAT - into a single percentage. This is a percentage of the fee amount, not a per-Wh rate. - elecfee_feedin_amt_wh: Total fixed deduction from feed-in energy per Wh [amount/Wh]. This is the accumulation of all fixed per-Wh charges deducted from feed-in energy - such as metering fees or grid-operator handling "charges - into a single amount. Applied after the percentage-based deduction, i.e. it reduces the price by a flat amount per Wh rather than by a share of the raw price. - elecfee_feedin_percent_amt: Total percentage deducted from the raw feed-in price (spot price) [%]. This is the accumulation of all percentage-based deductions payable on the feed-in tariff - such as a marketing or balancing fee retained by the aggregator - into a single percentage. It is applied as `raw_price * (100 - percent) / 100`, i.e. it scales down the raw price rather than adding a surcharge to it. A new _apply_fee() method is added to the base class for ElecPrice and FeedInTariff to be used to add the fees in a consistent way. Fees are taken from the active ElecFee provider and applied to the raw prices given to the _apply_fee() method. The optional application of fees is added to: - ElecPriceAkkudoktor - ElecPriceFixed - ElecPriceEnergyCharts - ElecPriceSMARD - FeedInTariffEnergyCharts - FeedInTariffFixed - FeedInTariffSMARD The import providers ElecPriceImport and FeedInTariffImport do not apply fees by intentention. The following providers currently do not handle fees defined by ElecFee: - ElecPriceTibber - FeedInTariffAkkudoktor - FeedInTariffDvhubOnline - FeedInTariffTibber The tests for this feature are either added or existing tests are extended. The documentation was extended for the electricity fee provider settings. Besides this feature further improvements are added: * feat: add SMARD quarter-hour electricty price and feed-in tariff provider * feat: to_series method for TimeWindows and ValueTimeWindows Additional to to_array the time window sequence can now also produce a pandas series. Test have been extended to cover the series generation. * feat: use time windows in fixed feedin tariff provider Feedin tariff can now be configured by time windows - not a single value. * feat: EOSdash select for PVLib inverters and modules Provide PVLib inverter and module names in config selection. * feat: EOSdash lazy select for big option sets Add a new form for lazy selection of big option sets. Filtering and generation of the option set is done server-side. * fix: use raw data for ETS/ median prediction Use to raw time series data for ETS/ median prediction to avoid interference by e.g. dynamic grid charges. * fix: EOSdash config drops by type only on details resolve Drop configuration by type and path. Prevents dropping of configuration items with same type and level but different path. * fix: EOSdash configuration section closes on update Open section if searching or if last update touched this category — including updates on deeply nested sub-fields. * chore: make elecfeefixed, elecpricefixed and feedintarifffixed warn about no windows and default to 0 Missining configuration creates default 0 value and a warning instead of an exception. * fix: test setup for providers Reset db state on each test run. * chore: improve config option naming for elecpricefixed. * chore: adapt elecpricefixed test to changed time_windows naming * chore: factorized common price provider helpers to priceabc.py Factorized common price provider helpers to priceabc.py. Add tests for these helpers. Reduce/ change testing of elecpriceabc.py and feedintariffabc.py to cover only specifics. Rest of testing is already covered by test_priceabc.py. * chore: update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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894790f577 |
feat: add pvlib pv forecast provider (#1214)
Add a PV forecast provider that calculates the forecast using a PVLib system model and weather forecast from the EOS weather forecast provider. Additional module and inverter models can be easily added as the database is build from PVLib and SAM databases and a bundled csv file. The module model and inververt model names are provided by new endpoints to be used in configuration. The provider is based on the fantastic work of EMHASS. See https://github.com/davidusb-geek/emhass/blob/master/src/emhass/forecast.py A short description of the provider is added to the documentation. Besides the new features there are the fixes and improvements: * feat: improve EOSdash config page * fix: kex_to_series for start_datetime Make key_to_series always start the series at start_datetime. * fix: default provider for GENETIC and GENETIC0 optimization To make the default less dependent on internet servers (with API changes and availability issues) the default for PVForecast is set to PVForecastPVLib and for ElecPrice to ElecPriceFixed. The default weather provider is changed to OpenMeteo. * fix: EOSdash display resampled prediction values Make EOSdash display resampled prediction values where resampling fits to the prediction value type. Use bar width that fits to 15 minutes value samples. * chore: add a UI hints system to EOSdash The UI hints system eases the definition of forms for configuration items. There are also forms for items in maps and lists. These forms allow to add and delete items to/ from maps and lists. The forms ensure that all required fields of newly added items are filled. * chore: Create an enum for valid optimization algorithms * chore. Make config also provide the available energy management modes. Used for configuration hints. * chore: Randomize default device id in configuration Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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7e5aa2f218 |
feat: add Tibber feed-in tariff provider (#1189)
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The `FeedInTariffTibber` provider requests `priceInfo` and `priceInfoRange` with `resolution: QUARTER_HOURLY` and preserves the native 15-minute timestamps. It uses Tibber's `energy` spot-price component without the `tax` part or EOS electricity-price charges. The end-customer `total` component is deliberately ignored. The provider deliberately rejects hourly API responses instead of silently repeating them. It reuses `elecprice.tibber.access_token` and `elecprice.tibber.home_id`, so no duplicate credentials are needed. Signed-off-by: Andreas Schmitz <akkudoktor.net> Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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4c8a8e40f5 |
feat: add Akkudoktor feed-in provider (#1187)
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The `FeedInTariffAkkudoktor` provider uses raw day-ahead market prices from `https://api.akkudoktor.net/prices` as `feed_in_tariff_wh`. It does not add electricity import charges or VAT. Published prices are extended to the configured prediction horizon with the same seasonal ETS or median fallback used by the Akkudoktor electricity-price provider. The Akkudoktor endpoint currently forwards hourly market prices from aWATTar. With a 15-minute optimization interval, EOS holds each hourly price constant for its four quarter-hour slots. This keeps the slot grid consistent but does not create genuine quarter-hour market prices. Signed-off-by: Andreas Schmitz <akkudoktor.net> Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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ed61918fe0 |
feat: add EnergyCharts feed-in tariff provider (#1165)
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The `FeedInTariffEnergyCharts` provider uses the raw Energy-Charts day-ahead market price as the feed-in tariff. It stores prices in `feed_in_tariff_wh` without adding electricity import charges or VAT. The data is loaded from the Energy-Charts `/price` endpoint for the configured bidding zone. The native Energy-Charts resolution, including quarter-hour data, is retained. Energy-Charts usually supplies prices only for the published day-ahead period. If that data does not cover the complete configured prediction horizon, the provider extends it as follows: - With more than 800 hours of history, an ETS (Holt-Winters exponential smoothing) forecast with weekly seasonality is used. - With more than 168 hours of history, an ETS forecast with daily seasonality is used. - With less history, the median of the available values is used as a constant fallback. The seasonal periods are adjusted to the source resolution. For example, quarter-hour data uses four values per hour. Values already supplied by Energy-Charts are kept unchanged; only missing future slots after the last published price are forecast. Consequently, a 15-minute optimization uses four forecast values per hour without converting them to hourly averages. Signed-off-by: Andreas Schmitz <akkudoktor.net> Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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3bb0e02aed |
fix: provider settings in top level field of configuration (#1161)
Configuration for some providers was given in the sub-field `provider_settings` combining the settings of several providers. Pydantic does not understand this very well and the configuration became cumbersome, especially in EOSdash. All provider settings from the `provider-settings` sub-field are now lifted to the top level field of the configuration. The changes are automatically migrated in the configuration. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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75548990e1 |
feat: add cloud PV forecast providers: pvnode.com, forecast-solar and solcast (#1150)
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This PR adds three native PV power forecast providers, giving operators more
cloud forecast sources to choose from via pvforecast.provider, alongside the
existing PVForecastAkkudoktor, PVForecastVrm and PVForecastImport:
PVForecastPVNode — native 15-minute forecasts from the pvnode.com
V2 API. Saved-site mode (GET /v2/forecast/{site_id}) where the operator enters their API key +
site id, or inline mode (POST /v2/forecast/inline) using the configured planes.
PVForecastForecastSolar — the free Forecast.Solar API (no key
required for the public endpoint). Multi-plane systems issue one request per plane and the
instantaneous powers are summed per timestamp.
PVForecastSolcast — the Solcast rooftop-site API (API key + resource id).
Implementation notes
All three populate the existing pvforecast_ac_power (and mirror pvforecast_dc_power)
prediction keys, so they slot into the optimizer unchanged.
Timezone handling: pvnode and Forecast.Solar return site-local wall-clock timestamps with an
IANA timezone field, which are resolved to absolute instants before resampling; Solcast returns
UTC period_end and is normalised to the period start.
Each provider follows the existing provider pattern (provider_id(), _request_forecast() with
@cache_in_file, _update_data()), is registered in pvforecast.py and prediction.py, and is
upstream-neutral.
Validation
27 unit tests (timezone resolution, null/zero handling, kW→W and period-start conversion,
azimuth conversion, multi-plane summation, request URL/auth, HTTP-error handling).
ruff check (F/D/S/bandit) and ruff format clean.
Each provider was additionally validated against its live API with a real plant, confirming
the response shapes (pvnode: 288 native 15-min slots; Forecast.Solar: instantaneous watts;
Solcast: kW estimates with period_end/PT30M).
Documentation
Provider descriptions and configuration examples added to docs/akkudoktoreos/prediction.md.
CHANGELOG.md entry under Unreleased.
Regenerated docs/_generated/configpvforecast.md and configexample.md.
Notes for reviewers
Forecast.Solar's free endpoint is rate-limited (12 req/hour) and Solcast's free tier limits daily
calls; both providers rely on the standard 1-hour cache_in_file TTL to stay within budget.
Authors:
The code is created by Christin. Only minor adaptions by Bobby.
Signed-off-by: Christin <info@bikinibottom.capital>
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: Christin <info@bikinibottom.capital>
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Bobby NoelteandGitHub
|
eb9e966de9 |
fix: move data management to async (#1015)
FAstAPI is an async framework. Data may be imported and exported, load and save, set and get asynchronously. Prevent interleaving data operations to corrupt the data. In the previous design sync and async data access was intermixed leading to data corruption. The basic data classes DataSequence and DataContainer and the derived classes like Provider and Measurement now are async. Data access is protected by several async locks. To support the async design of the data classes the database interface became async. The energy management is also adapted to the new async design. Optimization is still off-loaded to another thread, but the prepration for the optimization and the post optimization actions now follow the async design. Adapter operations are now also protected by async locks. Tests were adapted to the async design and new tests were created. Besides this major fix several other improvements and fixes are included in this PR. * fix: key_to_dict/list/array only regard data records with key value set. Before the exclusion of no value data records was only done if the dropna flag was set. * fix: test for visual result pdf generation Due to updates in the library the generated charts text was a little bit different. Adapt the test to create the comaprison pdf in the test data durectory and update the reference pdf. * chore: Remove MutableMapping from DataSequence and DataContainer. Mutable Mapping does not fit to the now async design. * chore: Add NoDB database backend This backend implements the full database backend interface but performs no actual persistence. It is intended for configurations where database persistence is disabled (`provider=None`). * chore: Improve measurement data import testing with real world scenarios. Added two new endpoints to support testing. * chore: Add mermaid to supported documentation tools * chore: Add documentation about async design * chore: Add documentation about generic data handling Covers the basics of measurement and prediction time series data handling. * chore: Add empty lines around markdown lists. * chore: sync pre-commit config to updated package versions Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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6f28022ed0 |
fix: optimization fail after restart (#1007)
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Fix documentation for the loadforecast_power_w key. Fix documentation to explain the usage of import file/ JSON string to primarily initialise prediction data. Fix code scanning alert no. 6: URL redirection from remote source Enable to automatically save the configuration to the configuration file by default, which is a widespread user expectation. Make the genetic parameters non optional for better pydantic compliance. Update: - bump pytest to 9.0.3 - bump pillow to 12.2.0 - bump platformdirs to 4.9.6 - bump typespyyaml to 6.0.12.20260408 - bump tzfpy to 1.2.0 - bump pydantic to 2.13.0 - bump types-requests to 2.33.0.20260408 Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Normann <github@koldrack.com> |
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Bobby NoelteandGitHub
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b67148c47f |
feat: make home assistant add-on run optimization by default (#940)
When running as Home Assistant add-on the only viable usage is running with cyclic optimization. Make this the default to als propvide a better experience for first time users. The optimization will start with demo data, which also helps to configure Akkudoktor-EOS to the personal usage. The disabling of the automatic energy management is now an explicit mode "DISABLED". Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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8a9aec6d57 |
feat: add openmeteo weather provider (#939)
Add OpenMeteo to the selectable weather prediction providers. Also add tests and documentation. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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cf477d91a3 |
feat: add fixed electricity prediction with time window support (#930)
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Add a fixed electricity prediction that supports prices per time window.
The time windows may flexible be defined by day or date.
The prediction documentation is updated to also cover the ElecPriceFixed
provider.
The feature includes several changes that are not directly related to the
electricity price prediction implementation but are necessary to keep
EOS running properly and to test and document the changes.
* feat: add value time windows
Add time windows with an associated float value.
* feat: harden eos measurements endpoints error detection and reporting
Cover more errors that may be raised during endpoint access. Report the
errors including trace information to ease debugging.
* feat: extend server configuration to cover all arguments
Make the argument controlled options also available in server configuration.
* fix: eos config configuration by cli arguments
Move the command line argument handling to config eos so that it is
excuted whenever eos config is rebuild or reset.
* chore: extend measurement endpoint system test
* chore: refactor time windows
Move time windows to configabc as they are only used in configurations.
Also move all tests to test_configabc.
* chore: provide config update errors in eosdash with summarized error text
If there is an update error provide the error text as a summary. On click
provide the full error text.
* chore: force eosdash ip address and port in makefile dev run
Ensure eosdash ip address and port are correctly set for development runs.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
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 Christopher NadlerandGitHub
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3ccc25d731 |
Adds inverter AC/DC efficiency and break-even penalty (#888)
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* feat: add inverter AC/DC efficiency and break-even penalty * test: update tests/test_geneticoptimize.py with new ac_charge_break_even parameter * docs: update documentation * chore: update version numbers in configuration files to v0.2.0.dev2602272006923535 |
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Bobby NoelteandGitHub
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d446274129 |
fix: Adapt versioning scheme to Home Assistant and switch to uv (#896)
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Home Assistant expects versioning always increases numbers. Add a date component to the development version to comply with this expectation. The scheme is now 0.0.0.dev<date><hash>. Use uv for creating and managing the virtual environment for developement. This enourmously speeds up dependency updates. For this change dependency requirements are now solely handled in pyproject.toml. requirements.tx and requirements-dev.txt are deleted. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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6498c7dc32 |
Add database support for measurements and historic prediction data. (#848)
The database supports backend selection, compression, incremental data load, automatic data saving to storage, automatic vaccum and compaction. Make SQLite3 and LMDB database backends available. Update tests for new interface conventions regarding data sequences, data containers, data providers. This includes the measurements provider and the prediction providers. Add database documentation. The fix includes several bug fixes that are not directly related to the database implementation but are necessary to keep EOS running properly and to test and document the changes. * fix: config eos test setup Make the config_eos fixture generate a new instance of the config_eos singleton. Use correct env names to setup data folder path. * fix: startup with no config Make cache and measurements complain about missing data path configuration but do not bail out. * fix: soc data preparation and usage for genetic optimization. Search for soc measurments 48 hours around the optimization start time. Only clamp soc to maximum in battery device simulation. * fix: dashboard bailout on zero value solution display Do not use zero values to calculate the chart values adjustment for display. * fix: openapi generation script Make the script also replace data_folder_path and data_output_path to hide real (test) environment pathes. * feat: add make repeated task function make_repeated_task allows to wrap a function to be repeated cyclically. * chore: removed index based data sequence access Index based data sequence access does not make sense as the sequence can be backed by the database. The sequence is now purely time series data. * chore: refactor eos startup to avoid module import startup Avoid module import initialisation expecially of the EOS configuration. Config mutation, singleton initialization, logging setup, argparse parsing, background task definitions depending on config and environment-dependent behavior is now done at function startup. * chore: introduce retention manager A single long-running background task that owns the scheduling of all periodic server-maintenance jobs (cache cleanup, DB autosave, …) * chore: canonicalize timezone name for UTC Timezone names that are semantically identical to UTC are canonicalized to UTC. * chore: extend config file migration for default value handling Extend the config file migration handling values None or nonexisting values that will invoke a default value generation in the new config file. Also adapt test to handle this situation. * chore: extend datetime util test cases * chore: make version test check for untracked files Check for files that are not tracked by git. Version calculation will be wrong if these files will not be commited. * chore: bump pandas to 3.0.0 Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit) for the output dtype which may become datetime64[us] (before it was ns). Also numeric dtype detection is now more strict which needs a different detection for numerics. * chore: bump pydantic-settings to 2.12.0 pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests were adapted and a workaround was introduced. Also ConfigEOS was adapted to allow for fine grain initialization control to be able to switch off certain settings such as file settings during test. * chore: remove sci learn kit from dependencies The sci learn kit is not strictly necessary as long as we have scipy. * chore: add documentation mode guarding for sphinx autosummary Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc mode. * chore: adapt docker-build CI workflow to stricter GitHub handling Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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SchaafAlexanderandGitHub
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a25b336f19 |
chore: Node-RED adapter documentation (#829)
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Updated the documentation to describe the Node-RED adapter functionality |
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Bobby Noelte
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7c98383e1f |
chore: improve Home Assistant adapter
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Add documentation for home assistant adapter. Make adapter correctly set the measurement keys for PV production. Add adapter configuration for grid import and export measurements. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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58d70e417b |
feat: add Home Assistant and NodeRED adapters (#764)
Adapters for Home Assistant and NodeRED integration are added. Akkudoktor-EOS can now be run as Home Assistant add-on and standalone. As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard in Home Assistant. The fix includes several bug fixes that are not directly related to the adapter implementation but are necessary to keep EOS running properly and to test and document the changes. * fix: development version scheme The development versioning scheme is adaptet to fit to docker and home assistant expectations. The new scheme is x.y.z and x.y.z.dev<hash>. Hash is only digits as expected by home assistant. Development version is appended by .dev as expected by docker. * fix: use mean value in interval on resampling for array When downsampling data use the mean value of all values within the new sampling interval. * fix: default battery ev soc and appliance wh Make the genetic simulation return default values for the battery SoC, electric vehicle SoC and appliance load if these assets are not used. * fix: import json string Strip outer quotes from JSON strings on import to be compliant to json.loads() expectation. * fix: default interval definition for import data Default interval must be defined in lowercase human definition to be accepted by pendulum. * fix: clearoutside schema change * feat: add adapters for integrations Adapters for Home Assistant and NodeRED integration are added. Akkudoktor-EOS can now be run as Home Assistant add-on and standalone. As Home Assistant add-on EOS uses ingress to fully integrate the EOSdash dashboard in Home Assistant. * feat: allow eos to be started with root permissions and drop priviledges Home assistant starts all add-ons with root permissions. Eos now drops root permissions if an applicable user is defined by paramter --run_as_user. The docker image defines the user eos to be used. * feat: make eos supervise and monitor EOSdash Eos now not only starts EOSdash but also monitors EOSdash during runtime and restarts EOSdash on fault. EOSdash logging is captured by EOS and forwarded to the EOS log to provide better visibility. * feat: add duration to string conversion Make to_duration to also return the duration as string on request. * chore: Use info logging to report missing optimization parameters In parameter preparation for automatic optimization an error was logged for missing paramters. Log is now down using the info level. * chore: make EOSdash use the EOS data directory for file import/ export EOSdash use the EOS data directory for file import/ export by default. This allows to use the configuration import/ export function also within docker images. * chore: improve EOSdash config tab display Improve display of JSON code and add more forms for config value update. * chore: make docker image file system layout similar to home assistant Only use /data directory for persistent data. This is handled as a docker volume. The /data volume is mapped to ~/.local/share/net.akkudoktor.eos if using docker compose. * chore: add home assistant add-on development environment Add VSCode devcontainer and task definition for home assistant add-on development. * chore: improve documentation |
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Bobby NoelteandGitHub
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4c2997dbd6 |
feat: add bidding zone to energy charts price prediction (#765)
Energy charts supports bidding zones. Allow to specifiy the bidding zone in the configuration. Extend and simplify ElecPrice configuration structure and setup config migration to automatically update the configuration file. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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b01bb1c61c |
fix: load data for automatic optimization (#731)
Automatic optimization used to take the adjusted load data even if there were no measurements leading to 0 load values. Split LoadAkkudoktor into LoadAkkudoktor and LoadAkkudoktorAdjusted. This allows to select load data either purely from the load data database or load data additionally adjusted by load measurements. Some value names have been adapted to denote also the unit of a value. For better load bug squashing the optimization solution data availability was improved. For better data visbility prediction data can now be distinguished from solution data in the generic optimization solution. Some predictions that may be of interest to understand the solution were added. Documentation was updated to resemble the addition load prediction provider and the value name changes. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby Noelte
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18b580cabe |
fix: ensure EV charge rates settings available
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Allow charge rates for electric vehicle to be provided by the POST optimize endpoint. Create a default value in case neither the parameters nor the configuration provide charge rates. This is also to allow to migrate from 0.1.0 configuration format to actual one. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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Bobby NoelteandGitHub
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b397b5d43e |
fix: automatic optimization (#596)
This fix implements the long term goal to have the EOS server run optimization (or
energy management) on regular intervals automatically. Thus clients can request
the current energy management plan at any time and it is updated on regular
intervals without interaction by the client.
This fix started out to "only" make automatic optimization (or energy management)
runs working. It turned out there are several endpoints that in some way
update predictions or run the optimization. To lock against such concurrent attempts
the code had to be refactored to allow control of execution. During refactoring it
became clear that some classes and files are named without a proper reference
to their usage. Thus not only refactoring but also renaming became necessary.
The names are still not the best, but I hope they are more intuitive.
The fix includes several bug fixes that are not directly related to the automatic optimization
but are necessary to keep EOS running properly to do the automatic optimization and
to test and document the changes.
This is a breaking change as the configuration structure changed once again and
the server API was also enhanced and streamlined. The server API that is used by
Andreas and Jörg in their videos has not changed.
* fix: automatic optimization
Allow optimization to automatically run on configured intervals gathering all
optimization parameters from configuration and predictions. The automatic run
can be configured to only run prediction updates skipping the optimization.
Extend documentaion to also cover automatic optimization. Lock automatic runs
against runs initiated by the /optimize or other endpoints. Provide new
endpoints to retrieve the energy management plan and the genetic solution
of the latest automatic optimization run. Offload energy management to thread
pool executor to keep the app more responsive during the CPU heavy optimization
run.
* fix: EOS servers recognize environment variables on startup
Force initialisation of EOS configuration on server startup to assure
all sources of EOS configuration are properly set up and read. Adapt
server tests and configuration tests to also test for environment
variable configuration.
* fix: Remove 0.0.0.0 to localhost translation under Windows
EOS imposed a 0.0.0.0 to localhost translation under Windows for
convenience. This caused some trouble in user configurations. Now, as the
default IP address configuration is 127.0.0.1, the user is responsible
for to set up the correct Windows compliant IP address.
* fix: allow names for hosts additional to IP addresses
* fix: access pydantic model fields by class
Access by instance is deprecated.
* fix: down sampling key_to_array
* fix: make cache clear endpoint clear all cache files
Make /v1/admin/cache/clear clear all cache files. Before it only cleared
expired cache files by default. Add new endpoint /v1/admin/clear-expired
to only clear expired cache files.
* fix: timezonefinder returns Europe/Paris instead of Europe/Berlin
timezonefinder 8.10 got more inaccurate for timezones in europe as there is
a common timezone. Use new package tzfpy instead which is still returning
Europe/Berlin if you are in Germany. tzfpy also claims to be faster than
timezonefinder.
* fix: provider settings configuration
Provider configuration used to be a union holding the settings for several
providers. Pydantic union handling does not always find the correct type
for a provider setting. This led to exceptions in specific configurations.
Now provider settings are explicit comfiguration items for each possible
provider. This is a breaking change as the configuration structure was
changed.
* fix: ClearOutside weather prediction irradiance calculation
Pvlib needs a pandas time index. Convert time index.
* fix: test config file priority
Do not use config_eos fixture as this fixture already creates a config file.
* fix: optimization sample request documentation
Provide all data in documentation of optimization sample request.
* fix: gitlint blocking pip dependency resolution
Replace gitlint by commitizen. Gitlint is not actively maintained anymore.
Gitlint dependencies blocked pip from dependency resolution.
* fix: sync pre-commit config to actual dependency requirements
.pre-commit-config.yaml was out of sync, also requirements-dev.txt.
* fix: missing babel in requirements.txt
Add babel to requirements.txt
* feat: setup default device configuration for automatic optimization
In case the parameters for automatic optimization are not fully defined a
default configuration is setup to allow the automatic energy management
run. The default configuration may help the user to correctly define
the device configuration.
* feat: allow configuration of genetic algorithm parameters
The genetic algorithm parameters for number of individuals, number of
generations, the seed and penalty function parameters are now avaliable
as configuration options.
* feat: allow configuration of home appliance time windows
The time windows a home appliance is allowed to run are now configurable
by the configuration (for /v1 API) and also by the home appliance parameters
(for the classic /optimize API). If there is no such configuration the
time window defaults to optimization hours, which was the standard before
the change. Documentation on how to configure time windows is added.
* feat: standardize mesaurement keys for battery/ ev SoC measurements
The standardized measurement keys to report battery SoC to the device
simulations can now be retrieved from the device configuration as a
read-only config option.
* feat: feed in tariff prediction
Add feed in tarif predictions needed for automatic optimization. The feed in
tariff can be retrieved as fixed feed in tarif or can be imported. Also add
tests for the different feed in tariff providers. Extend documentation to
cover the feed in tariff providers.
* feat: add energy management plan based on S2 standard instructions
EOS can generate an energy management plan as a list of simple instructions.
May be retrieved by the /v1/energy-management/plan endpoint. The instructions
loosely follow the S2 energy management standard.
* feat: make measurement keys configurable by EOS configuration.
The fixed measurement keys are replaced by configurable measurement keys.
* feat: make pendulum DateTime, Date, Duration types usable for pydantic models
Use pydantic_extra_types.pendulum_dt to get pydantic pendulum types. Types are
added to the datetimeutil utility. Remove custom made pendulum adaptations
from EOS pydantic module. Make EOS modules use the pydantic pendulum types
managed by the datetimeutil module instead of the core pendulum types.
* feat: Add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil.
The time windows are are added to support home appliance time window
configuration. All time classes are also pydantic models. Time is the base
class for time definition derived from pendulum.Time.
* feat: Extend DataRecord by configurable field like data.
Configurable field like data was added to support the configuration of
measurement records.
* feat: Add additional information to health information
Version information is added to the health endpoints of eos and eosDash.
The start time of the last optimization and the latest run time of the energy
management is added to the EOS health information.
* feat: add pydantic merge model tests
* feat: add plan tab to EOSdash
The plan tab displays the current energy management instructions.
* feat: add predictions tab to EOSdash
The predictions tab displays the current predictions.
* feat: add cache management to EOSdash admin tab
The admin tab is extended by a section for cache management. It allows to
clear the cache.
* feat: add about tab to EOSdash
The about tab resembles the former hello tab and provides extra information.
* feat: Adapt changelog and prepare for release management
Release management using commitizen is added. The changelog file is adapted and
teh changelog and a description for release management is added in the
documentation.
* feat(doc): Improve install and devlopment documentation
Provide a more concise installation description in Readme.md and add extra
installation page and development page to documentation.
* chore: Use memory cache for interpolation instead of dict in inverter
Decorate calculate_self_consumption() with @cachemethod_until_update to cache
results in memory during an energy management/ optimization run. Replacement
of dict type caching in inverter is now possible because all optimization
runs are properly locked and the memory cache CacheUntilUpdateStore is properly
cleared at the start of any energy management/ optimization operation.
* chore: refactor genetic
Refactor the genetic algorithm modules for enhanced module structure and better
readability. Removed unnecessary and overcomplex devices singleton. Also
split devices configuration from genetic algorithm parameters to allow further
development independently from genetic algorithm parameter format. Move
charge rates configuration for electric vehicles from optimization to devices
configuration to allow to have different charge rates for different cars in
the future.
* chore: Rename memory cache to CacheEnergyManagementStore
The name better resembles the task of the cache to chache function and method
results for an energy management run. Also the decorator functions are renamed
accordingly: cachemethod_energy_management, cache_energy_management
* chore: use class properties for config/ems/prediction mixin classes
* chore: skip debug logs from mathplotlib
Mathplotlib is very noisy in debug mode.
* chore: automatically sync bokeh js to bokeh python package
bokeh was updated to 3.8.0, make JS CDN automatically follow the package version.
* chore: rename hello.py to about.py
Make hello.py the adapted EOSdash about page.
* chore: remove demo page from EOSdash
As no the plan and prediction pages are working without configuration, the demo
page is no longer necessary
* chore: split test_server.py for system test
Split test_server.py to create explicit test_system.py for system tests.
* chore: move doc utils to generate_config_md.py
The doc utils are only used in scripts/generate_config_md.py. Move it there to
attribute for strong cohesion.
* chore: improve pydantic merge model documentation
* chore: remove pendulum warning from readme
* chore: remove GitHub discussions from contributing documentation
Github discussions is to be replaced by Akkudoktor.net.
* chore(release): bump version to 0.1.0+dev for development
* build(deps): bump fastapi[standard] from 0.115.14 to 0.117.1
bump fastapi and make coverage version (for pytest-cov) explicit to avoid pip break.
* build(deps): bump uvicorn from 0.36.0 to 0.37.0
BREAKING CHANGE: EOS configuration changed. V1 API changed.
- The available_charge_rates_percent configuration is removed from optimization.
Use the new charge_rate configuration for the electric vehicle
- Optimization configuration parameter hours renamed to horizon_hours
- Device configuration now has to provide the number of devices and device
properties per device.
- Specific prediction provider configuration to be provided by explicit
configuration item (no union for all providers).
- Measurement keys to be provided as a list.
- New feed in tariff providers have to be configured.
- /v1/measurement/loadxxx endpoints are removed. Use generic mesaurement endpoints.
- /v1/admin/cache/clear now clears all cache files. Use
/v1/admin/cache/clear-expired to only clear all expired cache files.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
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a4cce85ebd |
add configurable VAT rate for electricity price calculations (#680)
* add configurable VAT rate for electricity price calculations * add VAT rate configuration for electricity price calculations in docs * added vat_rate * fix: Format VAT rate field definition according to ruff formatting standards --------- Co-authored-by: Börni <kontakt@bernhardhientz.com> |
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 redmoon2711andGitHub
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8e69caba73 |
feat(VRM forecast): add load and pv forecast by VRM API (#611)
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Add support for fetching forecasts from the VRM API by Victron Energy. Retrieve forecasts for PV generation and total load of a Victron system from the internet. Tests for the new modules have been added, and the documentation has been updated accordingly. Signed-off-by: redmoon2711 <redmoon2711@gmx.de> |
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redmoon2711andGitHub
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eb23123bba |
fix(ElecPriceEnergyCharts): get history series, update docs (#606)
Signed-off-by: redmoon2711 <redmoon2711@gmx.de> |
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redmoon2711andGitHub
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8c56410338 |
Add new electricity price provider: Energy-Charts #381 (#590)
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* feat(ElecPriceEnergyCharts): Add new electricity price provider: Energy-Charts * feat(ElecPriceEnergyCharts): update data only if needed * test(elecpriceforecast): add test for energycharts * docs(predictions.md): add ElecPriceEnergyCharts Provider Signed-off-by: redmoon2711 <redmoon2711@gmx.de> |
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Bobby NoelteandGitHub
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bd38b3c5ef |
fix: logging, prediction update, multiple bugs (#584)
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* Fix logging configuration issues that made logging stop operation. Switch to Loguru logging (from Python logging). Enable console and file logging with different log levels. Add logging documentation. * Fix logging configuration and EOS configuration out of sync. Added tracking support for nested value updates of Pydantic models. This used to update the logging configuration when the EOS configurationm for logging is changed. Should keep logging config and EOS config in sync as long as all changes to the EOS logging configuration are done by set_nested_value(), which is the case for the REST API. * Fix energy management task looping endlessly after the second update when trying to update the last_update datetime. * Fix get_nested_value() to correctly take values from the dicts in a Pydantic model instance. * Fix usage of model classes instead of model instances in nested value access when evaluation the value type that is associated to each key. * Fix illegal json format in prediction documentation for PVForecastAkkudoktor provider. * Fix documentation qirks and add EOS Connect to integrations. * Support deprecated fields in configuration in documentation generation and EOSdash. * Enhance EOSdash demo to show BrightSky humidity data (that is often missing) * Update documentation reference to German EOS installation videos. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> |
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 rompicandGitHub
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aa39ff475c |
fix: add required fields to example optimization request (#574)
* fix: add required fields to example optimization request * fix: documentatin of parameters |
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rompicandGitHub
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46e078fce1 | chore: typos and consistant spelling of Home Assistant (#563) | ||
 MarcoandBobby Noelte
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f8c9fc191a | chore: Typos |