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		题名	作者	年代	出处	被引量
	1	System Impact Study for the Interconnection of Wind Generation andUtility System显示文摘	Chai Chompoo-inwai Wei-Jen Lee Pradit Fuangfoo		0，，：	1
	2	System impact study for the interconnection of wind generation and utility system显示文摘	Chompoo-Inwai Chai Lee Wei-Jen Fuangfoo Pradit	2005	IEEE Trans on Industry Applications2005，41，1：	1
	3	Transmission congestion management during transition period of electricity deregulation in Thailand显示文摘	Chompoo-Inwai Chai Yingvivatanapong Chitra Fuangfoo Pradit	2007	IEEE Trans on Industry Applications2007，43，6：	1
	4	System impact study for the interconnection of wind generation and utility system显示文摘	Chai Chompoo-inwai Wei-Jen Lee Pradit Fuangfoo	2005	IEEE Transactions on Industry Applications2005，41，1：	1
	5	System impact study for the interconnection of wind generation and utility system显示文摘	Chai Chompoo-inwai Lee Wei-Jen Pradit Fuangfoo	2005	IEEE Transactions on Industry Appli-cations2005，41，1：	1
	6	Adaptive meta-learning extreme learning machine with golden eagle optimization and logistic map for forecasting the incomplete data of solar iradiance显示文摘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.	Sarunyoo Boriratrit Pradit Fuangfoo Chitchai Srithapon Rongrit Chatthaworn	2023	Energy and AI2023，13，3：	0

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