Ultra-Short-Term Regional PV Power Forecasting Based on Fluctuation Pattern Recognition with Satellite Images

Regional photovoltaic (PV) power forecasting provides a foundation for grid management and trading in the power markets. To tackle the deficiency of conventional regional PV power modeling methods, such as the problem of challenging to select useful meteorological information and the problem that a single model cannot fully learn the complex and diverse fluctuation characteristics of power curves, an ultra-short-term regional PV power forecasting framework assembled by fusing fluctuation pattern recognition (FPR) and deep learning modeling under a fluctuation pattern prediction (FPP) model was proposed. First of all, the fluctuation characteristics of the regional PV power curves were extracted, including sampling loss area, mean value, standard deviation, and third derivative. Then, a K-means algorithm based FPR model was established to obtained three kinds of patterns. Moreover, each pattern of PV data was trained by long-short-term memory network (LSTM) for different forecasting models, separately. Finally, the FPP model based on convolutional neural network (CNN) was proposed to predict the fluctuation pattern of the future PV power with historical satellite images as input. Simulation results show that the proposed forecasting method is more accurate than other traditional models.