* chore: improve plan solution display
Add genetic optimization results to general solution provided by EOSdash plan display.
Add total results.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
* fix: genetic battery and home appliance device simulation
Fix genetic solution to make ac_charge, dc_charge, discharge, ev_charge or
home appliance start time reflect what the simulation was doing. Sometimes
the simulation decided to charge less or to start the appliance at another
time and this was not brought back to e.g. ac_charge.
Make home appliance simulation activate time window for the next day if it can not be
run today.
Improve simulation speed.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
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Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
* All config now nested.
- Use default config from model field default values. If providers
should be enabled by default, non-empty default config file could
be provided again.
- Environment variable support with EOS_ prefix and __ between levels,
e.g. EOS_SERVER__EOS_SERVER_PORT=8503 where all values are case
insensitive.
For more information see:
https://docs.pydantic.dev/latest/concepts/pydantic_settings/#parsing-environment-variable-values
- Use devices as registry for configured devices. DeviceBase as base
class with for now just initializion support (in the future expand
to operations during optimization).
- Strip down ConfigEOS to the only configuration instance. Reload
from file or reset to defaults is possible.
* Fix multi-initialization of derived SingletonMixin classes.
* Discharge Mask Bug, Tests updated, simple Price Forecast with linear weighting
* Price Forecast with linear weighting, last value = highest weighting Discharge enforce when soc = 0 -> mask bug
* Inverter: Self consumption interpolator for better discharge_hour results
* Small penalty when EV 100% and charge >0
* Price Forceast (use mean of last 7 days instead of repeat)
* Price Prediction as JSON simulation output, config fixed electricty fees configurable + MyPy & Ruff
* optimization states for AC, DC and IDLE now similar probab. Also AC states taken from config. Maybe a single config option for AC and E-Auto States is sensefull.
* test_class_optimize: Update testdata
* Write pdf and json to test/testdata/new.... so it can be analyzed
manually or just copied as new expected result.
* workflow: Upload pytest optimization result artifacts (pdf, json)
* Migrate from Flask to FastAPI
* FastAPI migration:
- Use pydantic model classes as input parameters to the
data/calculation classes.
- Interface field names changed to constructor parameter names (for
simplicity only during transition, should be updated in a followup
PR).
- Add basic interface requirements (e.g. some values > 0, etc.).
* Update tests for new data format.
* Python requirement down to 3.9 (TypeGuard no longer needed)
* Makefile: Add helpful targets (e.g. development server with reload)
* Move API doc from README to pydantic model classes (swagger)
* Link to swagger.io with own openapi.yml.
* Commit openapi.json and check with pytest for changes so the
documentation is always up-to-date.
* Streamline docker
* FastAPI: Run startup action on dev server
* Fix config for /strompreis, endpoint still broken however.
* test_openapi: Compare against docs/.../openapi.json
* Move fastapi to server/ submodule
* See #187 for new repository structure.
* Integrated single_test_optimization into pytest to run a basic optimization test with tolerance set to 1e-6, ensuring quick detection of deviations.
* Added a long-run test (400 generations, like single_test_optimization), which can be triggered using --full-run in pytest.
* Mocked PDF creation in optimization tests and added a new PDF generation test with image comparison validation.
Note: Current tolerance is set to 1e-6; feedback on whether this tolerance is tight enough is welcome.
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Co-authored-by: Normann <github@koldrack.com>
Co-authored-by: Michael Osthege <michael.osthege@outlook.com>
