This app is for blending forecast together
The main application (forecast_blend/app.py)
- Loads different forecasts
- Blends them together using weights
- Saves them ready to be used by the api
- This is done for all GSPs
This was previously done in the API
We always update the ForecastValueLatest table, but we only update the ForecastValue table every 30 minutes
The blend is created by choosing the intraday model with the lowest expected MAE (whilst also considering the delay to each model run). This intraday model is blended into PVNet-DA unless PVNet-DA has a lower expect MAE in which case no intraday model is selected. This is then blended into National_XG if needed - but this should rarely be the case
graph TD;
subgraph App
S(Start) --> LF
LF(Load All Forecasts);
LF --> Filter(Filter Forecasts);
Filter --> Blend[Blend \n Expected values];
Blend --> N4(Blend Probabilistic \n- only for National);
N4 --> S(Save Forecast)
S --> F(Finish)
end
subgraph Blending
A(All Forecasts);
W2(Weights);
A --> SUT(Split unique 'target times' \n and not);
SUT --> |Unique Target times| B
SUT --> |Duplicated Target times| C(Loop over each \n target time)
C --> BW
W2 --> BW
BW(Blend using weights) --> SumCheck
SumCheck{At least one \n forecast available} --> |yes| B
SumCheck --> |no| Blend2(Blend forecast with \n next set of weights)
Blend2 --> SumCheck2
SumCheck2{At least one \n forecast available} --> |yes| B
SumCheck2 --> |no| BB(Break)
B(Blended \n Forecast)
end
DB_URL: The database url you want to save the results toN_GSP: The number of gsps you want to pull. N_GSP=1 means only National is run.BLEND_NAME: The model name to save the blend under. Defaults to"blend".ALLOW_CLOUDCASTING: Whether to allow"pvnet_cloud"to be part of the blend. Defaults to FalseDATA_PLATFORM_HOST: The data platform host name. Default islocalhostDATA_PLATFORM_PORT: The data platform port. Default is50051.
Tests are in the tests folder and can be run using pytest
Might need to
export PYTHONPATH=${PYTHONPATH}:./forecast_blend