In production, the solar values look like they are lagged by an hour compared to actual. The rolling mean currently in the app would cause this effect.
By example:
When the forecast is going up the rolling mean systematically decreases all the values
da = xr.DataArray(
np.linspace(0, 11, num=12),
coords=[
pd.date_range(
"2024-01-01 00:00",
periods=12,
freq="15T",
)
],
dims="time",
)
da
Pull Request
Description
In production, the solar values look like they are lagged by an hour compared to actual. The rolling mean currently in the app would cause this effect.
By example:
When the forecast is going up the rolling mean systematically decreases all the values
Also when the forecast is going down the rolling mean will systematically increase all the values.