Andreas 78f6dfeb84 fix(elecprice): do not shorten the forecast by the source's own lag
The price series went flat towards the end of the horizon: a constant value
repeated for the last hours, exactly as long as the day-ahead source was behind.

The ETS extrapolation is appended after the last known price, but its length was
computed as `prediction.hours * slots_per_hour - covered_slots`, and
covered_slots is zero once the last known price lies before the run start. The
forecast therefore spanned prediction.hours measured from the last known price
rather than from now, and ended that much too early. Callers reading past that
point got the last record held constant.

With SMARD published up to 2026-09-08 23:45 and a run at 2026-09-09 13:00, the
forecast covered 09-09 00:00 to 09-12 00:00 while the horizon needed 09-12
13:00: 52 quarter-hour slots of flat price, right inside the trailing window the
terminal value curve is derived from.

The length is now measured from the last known value through to
`ems_start + prediction.hours`, which reduces to the previous formula whenever
the source is current. Both the electricity price and the feed-in tariff
provider had the same calculation.
2026-09-09 14:15:05 +02:00
2025-01-24 20:08:48 +01:00
2025-04-07 22:23:35 +02:00
2024-05-03 10:43:31 +02:00
2024-11-15 22:27:25 +01:00
2026-06-18 08:29:26 +02:00

AkkudoktorEOS AkkudoktorEOS

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:

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.

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

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

Supports aarch64 Architecture Supports amd64 Architecture

To install the Akkudoktor-EOS add-on in Home Assistant:

Open your Home Assistant instance and show the add add-on repository dialog with a specific repository URL pre-filled.

  1. Add the repository URL:

    In Home Assistant, go to:

    Settings → Add-ons → Add-on Store → ⋮ (top-right menu) → Repositories
    

    and enter the URL of this Git repository:

    https://github.com/Akkudoktor-EOS/EOS
    
  2. Install the add-on:

    After adding the repository, the add-on will appear in the Add-on Store. Click Install.

  3. Start the add-on:

    Once installed, click Start in the add-on panel.

  4. Access the dashboard:

    Click Open Web UI in the add-on panel.

  5. Configure EOS (optional): In the dashboard, go to:

    Config
    
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

  1. EOSdash (Recommended) - Web interface at http://localhost:8504
  2. Manual - Edit EOS.config.json directly
  3. 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.

Contributors

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

S
Description
This repository features an Energy Optimization System (EOS) that optimizes energy distribution, usage for batteries, heat pumps& household devices. It includes predictive models for electricity prices (planned), load forecasting& dynamic optimization to maximize energy efficiency & minimize costs. Founder Dr. Andreas Schmitz (YouTube @akkudoktor)
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