1.Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education (Shanghai Jiao Tong University), Shanghai 200240, China;2.East China Branch of State Grid Corporation of China, Shanghai 200120, China;3.China Electric Power Research Institute (Nanjing), Nanjing 210003, China
In recent years, deep learning has been applied to photovoltaic (PV) power forecasting and shows the advantage of high accuracy. However, there also exist problems such as time-consuming training. In order to handle these problems, an ultra-short-term forecasting model of PV power generation based on the self-organizing map (SOM) and broad learning system (BLS) is proposed. Firstly, the SOM is applied to cluster the PV data at each moment to extract the fluctuation characteristics during different periods and under different meteorological conditions. Secondly, a multi-step forecasting model of PV power generation based on BLS is constructed, which increases the number of neurons in width and is trained by calculating the pseudoinverse matrix, thus guaranteeing strong capability for fitting high-dimensional data while keeping high computation efficiency. Finally, the actual PV power generation data are used to carry out the case study. By comparing with commonly used ultra-short-term forecasting methods, the superiority of the proposed method in terms of forecasting accuracy and training efficiency is verified. This work is supported by National Natural Science Foundation of China (No. 52077136).
|||ZHOU Nan, XU Xiaoyuan, YAN Zheng, et al. Ultra-short-term Forecasting of Photovoltaic Power Generation Based on Broad Learning System[J]. Automation of Electric Power Systems,2021,45(1):55-64. DOI:10.7500/AEPS20200228002|