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  1. README.md +49 -0
  2. config.json +1 -0
  3. pytorch_model.bin +3 -0
README.md ADDED
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+ ---
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+ license: mit
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+ tags:
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+ - nowcasting
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+ - forecasting
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+ - timeseries
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+ - remote-sensing
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+ ---
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+
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+ # Nowcasting CNN
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+
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+ ## Model description
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+
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+ 3d conv model, that takes in different data streams
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+
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+ architecture is roughly
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+ 1. satellite image time series goes into many 3d convolution layers.
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+ 2. nwp time series goes into many 3d convolution layers.
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+ 3. Final convolutional layer goes to full connected layer. This is joined by
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+ other data inputs like
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+ - pv yield
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+ - time variables
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+ Then there ~4 fully connected layers which end up forecasting the
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+ pv yield / gsp into the future
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+
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+ ## Intended uses & limitations
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+
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+ Forecasting short term PV power for different regions and nationally in the UK
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+
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+ ## How to use
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+
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+ [More information needed]
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+
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+ ## Limitations and bias
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+
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+ [More information needed]
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+
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+ ## Training data
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+
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+ Training data is EUMETSAT RSS imagery over the UK, on-the-ground PV data, and NWP predictions.
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+
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+ ## Training procedure
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+
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+ [More information needed]
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+
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+ ## Evaluation results
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+
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+ [More information needed]
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+
config.json ADDED
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+ {"include_pv_or_gsp_yield_history": true, "include_nwp": true, "forecast_minutes": 120, "history_minutes": 30, "number_of_conv3d_layers": 4, "conv3d_channels": 32, "image_size_pixels": 24, "nwp_image_size_pixels": 64, "number_sat_channels": 11, "number_nwp_channels": 1, "fc1_output_features": 128, "fc2_output_features": 128, "fc3_output_features": 64, "output_variable": "gsp_yield", "embedding_dem": 8, "include_pv_yield_history": true, "include_future_satellite": false, "live_satellite_images": true, "gsp_forecast_minutes": 480, "gsp_history_minutes": 120, "include_sun": true}
pytorch_model.bin ADDED
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+ size 211198722