7 Commits

Include renames

Author	SHA1	Message	Date
5 people	70b72bfbce	feat(ems): notify a URL after every completed optimization (#1383) ... * 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> * feat(genetic): make the fitness cache size configurable 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: Claude Opus 5.5 <noreply@anthropic.com> * perf: import heavy optional dependencies on first use A GENETIC server with external (import) providers and EOSdash disabled loads several large packages at start that it never uses, which costs a noticeable part of the memory on 512 MB boards: - matplotlib via the GENETIC/GENETIC0 PDF report modules (server/eos.py) - statsmodels via the Holt-Winters fallback in prediction/priceabc.py - pvlib via the weather base class and the pvlib-based providers - bs4/lxml via the ClearOutside provider - scipy via the self-consumption interpolator singletons, which were loaded at import time - the GENETIC0 optimizer, imported by core/ems.py - tzfpy, whose polygon data the computed general.timezone loads Import these where they are used. The interpolator tables are loaded on first use (thread-safe). Nothing changes in what is computed. general.timezone is computed from latitude/longitude and cannot be set. Add general.timezone_override (IANA name, validated with pendulum): when set, general.timezone returns it and the lookup is skipped. When unset (default) the lookup is unchanged. Tests that patched the moved names now patch them at their source. A new test starts the server module in a subprocess and checks that none of these modules is loaded. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * perf(prediction): evaluate self-consumption tables without SciPy The GENETIC inverter (and the GENETIC0 one) evaluates a pickled scipy.interpolate.RegularGridInterpolator for the self-consumption probabilities. SciPy is used nowhere else in EOS, yet loading it costs about 35 MB RSS (21 MB anonymous) on Linux arm64, which every optimization run pays. Add utils/gridinterpolator.py: - LinearGridInterpolator: 2-D linear interpolation with the same steps as SciPy's linear 2-D path (interval search, normalised distances, corner weights summed in the same order, fill value outside the grid, NaN for NaN input). - load_grid_interpolator(): reads the shipped pickles with a restricted unpickler that maps the SciPy class to a stand-in and accepts only the NumPy globals needed for the arrays. SciPy is not imported, and the data files no longer depend on SciPy's private module paths. Results are bit-identical to SciPy on Linux (aarch64, checked on 3.6M points per table including the query pattern of the inverter). The macOS arm64 SciPy wheel fuses multiply-add, so there SciPy itself differs by <= 1.1e-16; GENETIC plans for fixed seeds are unchanged on both platforms. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(prediction): make the SciPy-free table evaluation opt-in Add optimization.self_consumption_interpolator (scipy | numpy, default scipy). The default keeps evaluating the self-consumption tables with SciPy's RegularGridInterpolator, built from the same grid, values and fill value the pickle holds, so results are unchanged. With numpy the tables are evaluated by LinearGridInterpolator and SciPy is never imported. The backend is read on every evaluation, so a config change applies to the next run. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(genetic): evaluate candidates in parallel worker processes A GENETIC run evaluated every candidate in the server process, so a run used one CPU core however many the machine had. On small ARM boards (4x Cortex-A55, thermally limited) a 300x200 run took 12-15 minutes. Candidates are now evaluated in up to two worker processes, forked once per run after the run is fully prepared. Each worker owns a copy of the prepared optimizer; only genomes and results cross the process boundary. The fitness cache stays in the server process: hits are answered there, and identical genomes in a batch are evaluated once, as a serial run would hit the cache for the repeats. Results are applied only after the whole batch returned. If a batch fails or times out, the run continues serially. For a fixed seed the result is identical to a serial run. optimization.genetic.workers sets the number of processes. Unset (default) is automatic: one CPU core always stays free and at most two are used (1-2 cores -> 1, 3 or more -> 2), capped by a cgroup v2 CPU limit of the process or any parent (container limit, systemd CPUQuota). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EYWR1HVM8a8src23xuyL3T * feat(ems): notify a URL after every completed optimization A client that consumes EOS plans (here DVhub) had to poll /v1/energy-management/optimization/solution to notice a new plan. That delays the plan by up to the poll interval and makes EOS serialize the complete solution on every poll, also while it is computing. ems.notify_url (default None = off) receives an HTTP POST with a small JSON event ({"event": "optimization_completed", "algorithm", "completed_at", "duration_s"}) when a run has published its solution. The POST runs in a daemon thread with a 5 s timeout, so a slow or unreachable receiver never delays energy management; a failure is logged once per failure streak. Only http(s) URLs are accepted. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EYWR1HVM8a8src23xuyL3T * fix(genetic): freeze the optimization configuration for the duration of a run The genetic optimization reads horizon_hours, tail_horizon_hours, interval_sec and prediction.hours from the live configuration on every access (control_slots, tail_slots, ... are properties). A client that updates the configuration while a run is in progress - DVhub shrinks or grows the control horizon as the known price horizon moves during the night - changed these values mid-run. Genomes and device arrays sized at the start then no longer matched the simulation loop and the final evaluation failed: IndexError: index 84 is out of bounds for axis 0 with size 84 Long runs on slow hardware hit this almost every night. optimize_ems() now takes a deep copy of the genetic configuration and the prediction hours at the start of a run, all reads in genetic.py go through it, and it is released when the run ends (also on errors). New values apply to the next run. Tests: tests/test_genetic_config_frozen_per_run.py reproduces the error without the fix (horizon 20 -> 24 mid-run: index 20 out of bounds) and passes with it; genetic/optimization tests 439 passed. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(genetic): pin evaluation workers, keep the server responsive Workers are pinned to the last `workers` CPU cores (new setting optimization.genetic.pin_workers, default on); the server process stays unpinned. The run never spreads beyond `workers` cores without a CPU quota, and the server and the rest of the system keep the other cores. No pinning without a spare core. With a CPU limit (container cpu.max, systemd CPUQuota) the automatic worker count now keeps a quarter core of it for the server process (limit 2 -> 1 worker, 2.5 -> 2): two workers used a 2-core limit fully. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EYWR1HVM8a8src23xuyL3T * fix(ems): tolerate settings without notify_url Test doubles and older configuration objects have no ems.notify_url. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EYWR1HVM8a8src23xuyL3T * 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 * docs: regenerate OpenAPI after merging the efficiency-curve fix * docs: regenerate OpenAPI after merging the efficiency-curve fix * docs: regenerate OpenAPI after merging the efficiency-curve fix * test(genetic): cover serial fallback on platforms without fork --------- 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>	2026-10-11 10:48:04 +02:00
Bobby NoelteandGitHub	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>	2026-08-07 13:13:17 +02:00
Bobby NoelteandGitHub	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>	2026-03-13 15:48:43 +01:00
Bobby NoelteandGitHub	237af5289f	fix: eosdash startup (#915) ... Adapt uvicorn log level to allowed levels. Ensure that EOSdash is started after EOS configuration is available. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2026-02-28 11:31:51 +01:00
Christopher NadlerandGitHub	3ccc25d731	Adds inverter AC/DC efficiency and break-even penalty (#888) ... * 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	2026-02-27 23:12:08 +01:00
Bobby NoelteandGitHub	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>	2026-02-22 14:12:42 +01:00
Bobby NoelteandGitHub	7bf9dd723e	chore: improve doc generation and test (#762) ... Improve documentation generation and add tests for documentation. Extend sphinx by todo directive. The configuration table is now split into several tables. The test is adapted accordingly. There is a new test that checks the docstrings to be compliant to the RST format as used by sphinx to create the documentation. We can not use Markdown in docstrings. The docstrings are adapted accordingly. An additional test checks that the documentation can be build with sphinx. This test takes very long is only enabled in full run (aka. ci) mode. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>	2025-11-13 22:53:46 +01:00