Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

Abstract

The motion of cloud over PV power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and PSO optimal weights-based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. Firstly, we use k-means clustering method and texture features based on Gray-Level Co-occurrence Matrix (GLCM) to classify the clouds. Secondly, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute is used for simulation, the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.

Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

“…By case study and the comparing between the other three traditional methods, the result proves the method could overcome the weaknesses of the traditional single method in the applicable scope and it is a more universal modeling suitable for most cloud scenes. In the future study based on the previous works [40]- [44], we should pay attention to the preprocessing of sky image, not only to ensure the operation speed, but also to ensure the image information is not lost too much. In addition, how to extract features so as to achieve more efficient classification of images is also another focus.…”

Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network

“…The stepwise forecast consists of two steps. First, solar radiation intensity [6][7][8][9][10] and temperature [11][12][13] are forecasted, which are then applied as inputs to forecast the PV power. Unlike the stepwise forecast method, direct forecast predicts PV power through the historical data of PV power and meteorological information, and its approaches are divided into three categories: (1) statistical model methods; (2) physical methods; and (3) artificial intelligent learning methods [14][15][16].…”