830af85fca
measurement:
- Add new measurement class to hold real world measurements.
- Handles load meter readings, grid import and export meter readings.
- Aggregates load meter readings aka. measurements to total load.
- Can import measurements from files, pandas datetime series,
pandas datetime dataframes, simple daetime arrays and
programmatically.
- Maybe expanded to other measurement values.
- Should be used for load prediction adaptions by real world
measurements.
core/coreabc:
- Add mixin class to access measurements
core/pydantic:
- Add pydantic models for pandas datetime series and dataframes.
- Add pydantic models for simple datetime array
core/dataabc:
- Provide DataImport mixin class for generic import handling.
Imports from JSON string and files. Imports from pandas datetime dataframes
and simple datetime arrays. Signature of import method changed to
allow import datetimes to be given programmatically and by data content.
- Use pydantic models for datetime series, dataframes, arrays
- Validate generic imports by pydantic models
- Provide new attributes min_datetime and max_datetime for DataSequence.
- Add parameter dropna to drop NAN/ None values when creating lists, pandas series
or numpy array from DataSequence.
config/config:
- Add common settings for the measurement module.
predictions/elecpriceakkudoktor:
- Use mean values of last 7 days to fill prediction values not provided by
akkudoktor.net (only provides 24 values).
prediction/loadabc:
- Extend the generic prediction keys by 'load_total_adjusted' for load predictions
that adjust the predicted total load by measured load values.
prediction/loadakkudoktor:
- Extend the Akkudoktor load prediction by load adjustment using measured load
values.
prediction/load_aggregator:
- Module removed. Load aggregation is now handled by the measurement module.
prediction/load_corrector:
- Module removed. Load correction (aka. adjustment of load prediction by
measured load energy) is handled by the LoadAkkudoktor prediction and
the generic 'load_mean_adjusted' prediction key.
prediction/load_forecast:
- Module removed. Functionality now completely handled by the LoadAkkudoktor
prediction.
utils/cacheutil:
- Use pydantic.
- Fix potential bug in ttl (time to live) duration handling.
utils/datetimeutil:
- Added missing handling of pendulum.DateTime and pendulum.Duration instances
as input. Handled before as datetime.datetime and datetime.timedelta.
utils/visualize:
- Move main to generate_example_report() for better testing support.
server/server:
- Added new configuration option server_fastapi_startup_server_fasthtml
to make startup of FastHTML server by FastAPI server conditional.
server/fastapi_server:
- Add APIs for measurements
- Improve APIs to provide or take pandas datetime series and
datetime dataframes controlled by pydantic model.
- Improve APIs to provide or take simple datetime data arrays
controlled by pydantic model.
- Move fastAPI server API to v1 for new APIs.
- Update pre v1 endpoints to use new prediction and measurement capabilities.
- Only start FastHTML server if 'server_fastapi_startup_server_fasthtml'
config option is set.
tests:
- Adapt import tests to changed import method signature
- Adapt server test to use the v1 API
- Extend the dataabc test to test for array generation from data
with several data interval scenarios.
- Extend the datetimeutil test to also test for correct handling
of to_datetime() providing now().
- Adapt LoadAkkudoktor test for new adjustment calculation.
- Adapt visualization test to use example report function instead of visualize.py
run as process.
- Removed test_load_aggregator. Functionality is now tested in test_measurement.
- Added tests for measurement module
docs:
- Remove sphinxcontrib-openapi as it prevents build of documentation.
"site-packages/sphinxcontrib/openapi/openapi31.py", line 305, in _get_type_from_schema
for t in schema["anyOf"]: KeyError: 'anyOf'"
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Energy System Simulation and Optimization
This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.
Getting Involved
See CONTRIBUTING.md.
Installation
The project requires Python 3.9 or newer. Currently there are no official packages or images published.
Following sections describe how to locally start the EOS server on http://localhost:8503.
Run from source
Install dependencies in virtual environment:
Linux:
python -m venv .venv
.venv/bin/pip install -r requirements.txt
Windows:
python -m venv .venv
.venv\Scripts\pip install -r requirements.txt
Finally, start EOS fastapi server:
Linux:
.venv/bin/fastapi run --port 8503 src/akkudoktoreos/server/fastapi_server.py
Windows:
.venv\Scripts\fastapi run --port 8503 src/akkudoktoreos/server/fastapi_server.py
Docker
docker compose up --build
Configuration
This project uses the EOS.config.json file to manage configuration settings.
Default Configuration
A default configuration file default.config.json is provided. This file contains all the necessary configuration keys with their default values.
Custom Configuration
Users can specify a custom configuration directory by setting the environment variable EOS_DIR.
- If the directory specified by
EOS_DIRcontains an existingconfig.jsonfile, the application will use this configuration file. - If the
EOS.config.jsonfile does not exist in the specified directory, thedefault.config.jsonfile will be copied to the directory asEOS.config.json.
Configuration Updates
If the configuration keys in the EOS.config.json file are missing or different from those in default.config.json, they will be automatically updated to match the default settings, ensuring that all required keys are present.
Classes and Functionalities
This project uses various classes to simulate and optimize the components of an energy system. Each class represents a specific aspect of the system, as described below:
-
Battery: Simulates a battery storage system, including capacity, state of charge, and now charge and discharge losses. -
PVForecast: Provides forecast data for photovoltaic generation, based on weather data and historical generation data. -
Load: Models the load requirements of a household or business, enabling the prediction of future energy demand. -
Heatpump: Simulates a heat pump, including its energy consumption and efficiency under various operating conditions. -
Strompreis: Provides information on electricity prices, enabling optimization of energy consumption and generation based on tariff information. -
EMS: The Energy Management System (EMS) coordinates the interaction between the various components, performs optimization, and simulates the operation of the entire energy system.
These classes work together to enable a detailed simulation and optimization of the energy system. For each class, specific parameters and settings can be adjusted to test different scenarios and strategies.
Customization and Extension
Each class is designed to be easily customized and extended to integrate additional functions or improvements. For example, new methods can be added for more accurate modeling of PV system or battery behavior. Developers are invited to modify and extend the system according to their needs.
Server API
See the Swagger API documentation for detailed information: EOS OpenAPI Spec