A TCN-based Spatial-Temporal PV Forecasting Framework with Automated Detector Network Selection

This paper proposes a two-stage PV forecasting framework for MW-level PV farms based on Temporal Convolutional Network (TCN). In the day-ahead stage, inverter-level physics-based model is built to convert Numerical Weather Prediction (NWP) to hourly power forecasts. TCN works as the NWP blender to merge different NWP sources to improve the forecasting accuracy. In the real-time stage, TCN can leverage the spatial-temporal correlations between the target site and its neighbors to achieve intra-hour power forecasts. A scenario-based correlation analysis method is proposed to automatically identify the most contributive neighbors. Simulation results based on 95 PV farms in North Carolina demonstrate the accuracy and efficiency of the proposed method.