720809397c
* 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> * 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> * 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 --------- 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>
Build optimized energy management plans for your home automation
AkkudoktorEOS is a comprehensive solution for simulating and optimizing energy systems based on renewable sources. Optimize your photovoltaic systems, battery storage, load management, and electric vehicles while considering real-time electricity pricing.
Why use AkkudoktorEOS?
AkkudoktorEOS can be used to build energy management plans that are optimized for your specific setup of PV system, battery, electric vehicle, household load and electricity pricing. It can be integrated into home automation systems such as NodeRED, Home Assistant, EVCC.
🏘️ Community
We are an open-source community-driven project and we love to hear from you. Here are some ways to get involved:
-
GitHub Issue Tracker: discuss ideas and features, and report bugs.
-
Akkudoktor Forum: get direct suppport from the community.
What do people build with AkkudoktorEOS
The community uses AkkudoktorEOS to minimize grid energy consumption and to maximize the revenue from grid energy feed in with their home automation system.
- Andreas Schmitz, the Akkudoktor, uses EOS integrated in his NodeRED home automation system for OpenSource Energieoptimierung.
- Jörg, meintechblog, uses EOS for day-ahead optimization for time-variable energy prices. See: So installiere ich EOS von Andreas Schmitz
Why not use AkkudoktorEOS?
AkkudoktorEOS does not control your home automation assets. It must be integrated into a home automation system. If you do not use a home automation system or you feel uncomfortable with the configuration effort needed for the integration you should better use other solutions.
Quick Start
Upgrading an existing installation?
The next release includes a substantial energy-planning update: 15-minute GENETIC plans, EV departure targets, flexible household loads, optional battery export, and better measurement and PV-forecast tools. These features are currently unreleased.
Existing Home Assistant automations, Node-RED flows and custom API clients may need changes.
Devices now use stable IDs, the new optimizer reads hardware settings from EOS configuration,
and incomplete forecasts or outdated battery measurements can stop a run. The legacy
POST /optimize remains available with GENETIC0; the new POST /v1/optimize has a different
request format. Back up your configuration and data before updating.
Read the upgrade guide and changelog before switching an existing installation.
Start with Docker
Run EOS with Docker (access dashboard at http://localhost:8504):
docker run -d \
--name akkudoktoreos \
-p 8503:8503 \
-p 8504:8504 \
-e OPENBLAS_NUM_THREADS=1 \
-e OMP_NUM_THREADS=1 \
-e MKL_NUM_THREADS=1 \
-e EOS_SERVER__HOST=0.0.0.0 \
-e EOS_SERVER__EOSDASH_HOST=0.0.0.0 \
-e EOS_SERVER__EOSDASH_PORT=8504 \
--ulimit nproc=65535:65535 \
--ulimit nofile=65535:65535 \
--security-opt seccomp=unconfined \
akkudoktor/eos:latest
System Requirements
- Python: 3.11 or higher
- Architecture: amd64, aarch64 (armv8)
- OS: Linux, Windows, macOS
Note
: Other architectures (armv6, armv7) require manual compilation of dependencies with Rust and GCC.
Installation
Home Assistant add-on
To install the Akkudoktor-EOS add-on in Home Assistant:
-
Add the repository URL:
In Home Assistant, go to:
Settings → Add-ons → Add-on Store → ⋮ (top-right menu) → Repositoriesand enter the URL of this Git repository:
https://github.com/Akkudoktor-EOS/EOS -
Install the add-on:
After adding the repository, the add-on will appear in the Add-on Store. Click Install.
-
Start the add-on:
Once installed, click Start in the add-on panel.
-
Access the dashboard:
Click Open Web UI in the add-on panel.
-
Configure EOS (optional): In the dashboard, go to:
Config
Docker (Recommended)
docker pull akkudoktor/eos:latest
docker compose up -d
Access the API at http://localhost:8503 (docs at http://localhost:8503/docs)
From Source
git clone https://github.com/Akkudoktor-EOS/EOS.git
cd EOS
Linux:
python -m venv .venv
.venv/bin/pip install -r requirements.txt
.venv/bin/pip install -e .
.venv/bin/python -m akkudoktoreos.server.eos
Windows:
python -m venv .venv
.venv\Scripts\pip install -r requirements.txt
.venv\Scripts\pip install -e .
.venv\Scripts\python -m akkudoktoreos.server.eos
Configuration
EOS uses EOS.config.json for configuration. If the file doesn't exist, a default configuration is
created automatically.
Custom Configuration Directory
export EOS_DIR=/path/to/your/config
Configuration Methods
- EOSdash (Recommended) - Web interface at
http://localhost:8504 - Manual - Edit
EOS.config.jsondirectly - API - Use the Server API
See the documentation for all configuration options.
Port Configuration
Default ports: 8503 (API), 8504 (Dashboard)
If running on shared systems (e.g., Synology NAS), these ports may conflict with system services. Reconfigure port mappings as needed:
docker run -p 8505:8503 -p 8506:8504 ...
API Documentation
Interactive API docs available at:
- Swagger UI:
http://localhost:8503/docs - OpenAPI Spec: View Online
Resources
Contributing
We welcome contributions! See CONTRIBUTING for guidelines.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.