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An Ultra-short-term Forecasting Model for High-resolution Solar Irradiance Based on SOM and Deep Learning Algorithm

Solar irradiance forecasting is prerequisite for step-wise photovoltaic (PV) power predictions. In ultra-short-term time scale, the strong and diversified volatility of high-resolution irradiance data may severely restrict the accuracy of common forecasting models, which can be tackled through classified modeling and deep learning algorithm. In this paper, an ultra-short-term forecasting model for high-resolution solar irradiance data is proposed. The self-organizing mapping (SOM) is applied to realize data clustering and label prediction through the topology structure of irradiance sequence, then the deep learning algorithm is applied to establish the classified forecasting models corresponding to predicted labels. The method proposed in this paper contains 3 steps: firstly, the irradiance sequence with a time resolution of 5 minutes is divided into irradiance segments by per hour, and then are normalized. Secondly, the irradiance segments are classified based on SOM and the label of the data to be predicted is determined according to the topological structure of SOM. Thirdly, the data with the same label transformation is found to establish classified forecasting model based on the deep learning algorithm. A case study adopting the solar irradiance data from Sioux Falls, South Dakota, shows the proposed method presents better accuracy than direct prediction at different forecast time scales, and the deep learning algorithm has better forecasting performance.