EOS/tests/test_interpolator.py

import pytest

from akkudoktoreos.prediction.interpolator import get_eos_load_interpolator


def test_quarter_hour_energy_is_converted_back_to_same_mean_power():
    """Splitting hourly energy must not change the minute-load probability lookup."""
    interpolator = get_eos_load_interpolator()
    hourly_load_wh = 800.0
    hourly_pv_wh = 1200.0
    slot_duration_h = 0.25

    hourly = interpolator.calculate_expected_direct_consumption(hourly_load_wh, hourly_pv_wh)
    quarter_hour = interpolator.calculate_expected_direct_consumption(
        (hourly_load_wh / 4) / slot_duration_h,
        (hourly_pv_wh / 4) / slot_duration_h,
    )

    assert quarter_hour == pytest.approx(hourly)


def test_load_above_probability_grid_uses_highest_supported_distribution():
    """Out-of-range household load must not make self-consumption jump to zero."""
    interpolator = get_eos_load_interpolator()

    at_boundary = interpolator.calculate_self_consumption(3450.0, 5000.0)
    above_boundary = interpolator.calculate_self_consumption(4000.0, 5000.0)

    assert above_boundary == pytest.approx(at_boundary)
    assert above_boundary > 0.99


def test_expected_direct_consumption_accounts_for_subhourly_load_variation():
    """Expected overlap must be below the optimistic overlap of interval means."""
    interpolator = get_eos_load_interpolator()

    direct_power_w = interpolator.calculate_expected_direct_consumption(800.0, 1200.0)

    assert direct_power_w == pytest.approx(621.0, abs=2.0)
    assert 0.0 < direct_power_w < 800.0


@pytest.mark.parametrize(
    ("mean_load_power_w", "pv_power_w"),
    [(800.0, 1200.0), (1000.0, 500.0), (1500.0, 1500.0)],
)
def test_expected_direct_consumption_produces_conservative_energy_balance(
    mean_load_power_w, pv_power_w
):
    """Direct use, residual load and surplus must conserve both mean powers."""
    interpolator = get_eos_load_interpolator()

    direct_power_w = interpolator.calculate_expected_direct_consumption(
        mean_load_power_w, pv_power_w
    )
    residual_load_w = mean_load_power_w - direct_power_w
    pv_surplus_w = pv_power_w - direct_power_w

    assert 0.0 <= direct_power_w <= min(mean_load_power_w, pv_power_w)
    assert direct_power_w + residual_load_w == pytest.approx(mean_load_power_w)
    assert direct_power_w + pv_surplus_w == pytest.approx(pv_power_w)


def test_expected_direct_consumption_preserves_forecast_mean_at_high_pv():
    """A PV level above every normalized load bin covers the complete mean load."""
    interpolator = get_eos_load_interpolator()

    direct_power_w = interpolator.calculate_expected_direct_consumption(3000.0, 10000.0)

    assert direct_power_w == pytest.approx(3000.0)