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https://github.com/Akkudoktor-EOS/EOS.git
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b01bb1c61c
Automatic optimization used to take the adjusted load data even if there were no measurements leading to 0 load values. Split LoadAkkudoktor into LoadAkkudoktor and LoadAkkudoktorAdjusted. This allows to select load data either purely from the load data database or load data additionally adjusted by load measurements. Some value names have been adapted to denote also the unit of a value. For better load bug squashing the optimization solution data availability was improved. For better data visbility prediction data can now be distinguished from solution data in the generic optimization solution. Some predictions that may be of interest to understand the solution were added. Documentation was updated to resemble the addition load prediction provider and the value name changes. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
234 lines
7.0 KiB
Python
234 lines
7.0 KiB
Python
from unittest.mock import patch
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import numpy as np
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import pendulum
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import pytest
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from akkudoktoreos.core.ems import get_ems
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from akkudoktoreos.measurement.measurement import MeasurementDataRecord, get_measurement
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from akkudoktoreos.prediction.loadakkudoktor import (
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LoadAkkudoktor,
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LoadAkkudoktorAdjusted,
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LoadAkkudoktorCommonSettings,
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)
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from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
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@pytest.fixture
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def loadakkudoktor(config_eos):
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"""Fixture to initialise the LoadAkkudoktor instance."""
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settings = {
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"load": {
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"provider": "LoadAkkudoktor",
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"provider_settings": {
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"LoadAkkudoktor": {
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"loadakkudoktor_year_energy_kwh": "1000",
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},
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},
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},
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}
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config_eos.merge_settings_from_dict(settings)
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assert config_eos.load.provider == "LoadAkkudoktor"
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assert config_eos.load.provider_settings.LoadAkkudoktor.loadakkudoktor_year_energy_kwh == 1000
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return LoadAkkudoktor()
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@pytest.fixture
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def loadakkudoktoradjusted(config_eos):
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"""Fixture to initialise the LoadAkkudoktorAdjusted instance."""
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settings = {
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"load": {
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"provider": "LoadAkkudoktorAdjusted",
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"provider_settings": {
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"LoadAkkudoktor": {
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"loadakkudoktor_year_energy_kwh": "1000",
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},
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},
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},
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"measurement": {
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"load_emr_keys": ["load0_mr", "load1_mr"]
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}
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}
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config_eos.merge_settings_from_dict(settings)
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assert config_eos.load.provider == "LoadAkkudoktorAdjusted"
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assert config_eos.load.provider_settings.LoadAkkudoktor.loadakkudoktor_year_energy_kwh == 1000
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return LoadAkkudoktorAdjusted()
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@pytest.fixture
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def measurement_eos():
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"""Fixture to initialise the Measurement instance."""
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measurement = get_measurement()
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load0_mr = 500
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load1_mr = 500
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dt = to_datetime("2024-01-01T00:00:00")
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interval = to_duration("1 hour")
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for i in range(25):
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measurement.records.append(
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MeasurementDataRecord(
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date_time=dt,
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load0_mr=load0_mr,
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load1_mr=load1_mr,
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)
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)
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dt += interval
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load0_mr += 50
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load1_mr += 50
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assert compare_datetimes(measurement.min_datetime, to_datetime("2024-01-01T00:00:00")).equal
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assert compare_datetimes(measurement.max_datetime, to_datetime("2024-01-02T00:00:00")).equal
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return measurement
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@pytest.fixture
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def mock_load_profiles_file(tmp_path):
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"""Fixture to create a mock load profiles file."""
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load_profiles_path = tmp_path / "load_profiles.npz"
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np.savez(
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load_profiles_path,
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yearly_profiles=np.random.rand(365, 24), # Random load profiles
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yearly_profiles_std=np.random.rand(365, 24), # Random standard deviation
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)
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return load_profiles_path
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def test_loadakkudoktor_settings_validator():
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"""Test the field validator for `loadakkudoktor_year_energy_kwh`."""
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settings = LoadAkkudoktorCommonSettings(loadakkudoktor_year_energy_kwh=1234)
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assert isinstance(settings.loadakkudoktor_year_energy_kwh, float)
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assert settings.loadakkudoktor_year_energy_kwh == 1234.0
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settings = LoadAkkudoktorCommonSettings(loadakkudoktor_year_energy_kwh=1234.56)
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assert isinstance(settings.loadakkudoktor_year_energy_kwh, float)
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assert settings.loadakkudoktor_year_energy_kwh == 1234.56
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def test_loadakkudoktor_provider_id(loadakkudoktor):
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"""Test the `provider_id` class method."""
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assert loadakkudoktor.provider_id() == "LoadAkkudoktor"
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@patch("akkudoktoreos.prediction.loadakkudoktor.np.load")
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def test_load_data_from_mock(mock_np_load, mock_load_profiles_file, loadakkudoktor):
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"""Test the `load_data` method."""
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# Mock numpy load to return data similar to what would be in the file
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mock_np_load.return_value = {
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"yearly_profiles": np.ones((365, 24)),
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"yearly_profiles_std": np.zeros((365, 24)),
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}
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# Test data loading
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data_year_energy = loadakkudoktor.load_data()
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assert data_year_energy is not None
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assert data_year_energy.shape == (365, 2, 24)
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def test_load_data_from_file(loadakkudoktor):
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"""Test `load_data` loads data from the profiles file."""
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data_year_energy = loadakkudoktor.load_data()
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assert data_year_energy is not None
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@patch("akkudoktoreos.prediction.loadakkudoktor.LoadAkkudoktor.load_data")
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def test_update_data(mock_load_data, loadakkudoktor):
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"""Test the `_update` method."""
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mock_load_data.return_value = np.random.rand(365, 2, 24)
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# Mock methods for updating values
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ems_eos = get_ems()
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ems_eos.set_start_datetime(pendulum.datetime(2024, 1, 1))
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# Assure there are no prediction records
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loadakkudoktor.clear()
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assert len(loadakkudoktor) == 0
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# Execute the method
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loadakkudoktor._update_data()
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# Validate that update_value is called
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assert len(loadakkudoktor) > 0
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def test_calculate_adjustment(loadakkudoktoradjusted, measurement_eos):
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"""Test `_calculate_adjustment` for various scenarios."""
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data_year_energy = np.random.rand(365, 2, 24)
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# Call the method and validate results
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weekday_adjust, weekend_adjust = loadakkudoktoradjusted._calculate_adjustment(data_year_energy)
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assert weekday_adjust.shape == (24,)
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assert weekend_adjust.shape == (24,)
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data_year_energy = np.zeros((365, 2, 24))
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weekday_adjust, weekend_adjust = loadakkudoktoradjusted._calculate_adjustment(data_year_energy)
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assert weekday_adjust.shape == (24,)
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expected = np.array(
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[
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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100.0,
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]
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)
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np.testing.assert_array_equal(weekday_adjust, expected)
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assert weekend_adjust.shape == (24,)
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expected = np.array(
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[
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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]
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)
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np.testing.assert_array_equal(weekend_adjust, expected)
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def test_provider_adjustments_with_mock_data(loadakkudoktoradjusted):
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"""Test full integration of adjustments with mock data."""
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with patch(
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"akkudoktoreos.prediction.loadakkudoktor.LoadAkkudoktorAdjusted._calculate_adjustment"
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) as mock_adjust:
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mock_adjust.return_value = (np.zeros(24), np.zeros(24))
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# Test execution
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loadakkudoktoradjusted._update_data()
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assert mock_adjust.called
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