Solar energy has become crucial in producing electrical energy because it is inexhaustible and sustainable.However,its uncertain generation causes problems in power system operation.Therefore,solar irradiance forecast...Solar energy has become crucial in producing electrical energy because it is inexhaustible and sustainable.However,its uncertain generation causes problems in power system operation.Therefore,solar irradiance forecasting is significant for suitable controlling power system operation,organizing the transmission expansion planning,and dispatching power system generation.Nonetheless,the forecasting performance can be decreased due to the unfitted prediction model and lacked preprocessing.To deal with mentioned issues,this paper pro-poses Meta-Learning Extreme Learning Machine optimized with Golden Eagle Optimization and Logistic Map(MGEL-ELM)and the Same Datetime Interval Averaged Imputation algorithm(SAME)for improving the fore-casting performance of incomplete solar irradiance time series datasets.Thus,the proposed method is not only imputing incomplete forecasting data but also achieving forecasting accuracy.The experimental result of fore-casting solar irradiance dataset in Thailand indicates that the proposed method can achieve the highest coeffi-cient of determination value up to 0.9307 compared to state-of-the-art models.Furthermore,the proposed method consumes less forecasting time than the deep learning model.展开更多
文摘Solar energy has become crucial in producing electrical energy because it is inexhaustible and sustainable.However,its uncertain generation causes problems in power system operation.Therefore,solar irradiance forecasting is significant for suitable controlling power system operation,organizing the transmission expansion planning,and dispatching power system generation.Nonetheless,the forecasting performance can be decreased due to the unfitted prediction model and lacked preprocessing.To deal with mentioned issues,this paper pro-poses Meta-Learning Extreme Learning Machine optimized with Golden Eagle Optimization and Logistic Map(MGEL-ELM)and the Same Datetime Interval Averaged Imputation algorithm(SAME)for improving the fore-casting performance of incomplete solar irradiance time series datasets.Thus,the proposed method is not only imputing incomplete forecasting data but also achieving forecasting accuracy.The experimental result of fore-casting solar irradiance dataset in Thailand indicates that the proposed method can achieve the highest coeffi-cient of determination value up to 0.9307 compared to state-of-the-art models.Furthermore,the proposed method consumes less forecasting time than the deep learning model.
