This app is for blending forecast together

The main application (forecast_blend/app.py)

1. Loads different forecasts from Data Platform
2. Blends them together using weights
3. Saves them ready to be used by the api
4. This is done for all GSPs

This was previously done in the API

Details

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

Environment Variables

• N_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 False
• DATA_PLATFORM_HOST: The data platform host name. Default is localhost
• DATA_PLATFORM_PORT: The data platform port. Default is 50051.

Tests

Tests are in the tests folder and can be run using pytest

Might need to

export PYTHONPATH=${PYTHONPATH}:./forecast_blend