Solar Energy Forecasting in Short Term Based on the ASO-BPNN Model - Cao, Huazhen; Wang, Tianlin; Chen, Peidong; Cheng, Weitian; Cao, Yi; Liu, Zhiwen (Frontiers in Energy Research, 2022)
Accurate forecasting of short-term photovoltaic power output is vital for enhancing the operation efficiency of photovoltaic (PV) power station and ensuring the safety and stable operation of grid-connected PV plant. Therefore, a short-term power forecasting model based on back propagation neural network with atom search optimization optimizing the weights and thresholds. Meanwhile, Pearson correlation coefficient formula is introduced to screen the key meteorological factors and eliminate redundant factors, i.e., total irradiance, temperature, humidity, and direct irradiance are taken as the input of the prediction model. Moreover, Euclidean distance formula is used to establish customized training set for each test data which improve the dependability of the training set. Lastly, with the simulations of actual data from a solar farm in Yunnan, China, it is verified that the proposed ASO-BPNN model are competent to forecast the PV power generation.