干旱区生态环境知识资源中心(Arid): Short-term Photovoltaic Power Forecasting Based on Improved Firefly Algorithm to optimize support vector machine

Arid

DOI	10.1109/IS3C50286.2020.00095
	Short-term Photovoltaic Power Forecasting Based on Improved Firefly Algorithm to optimize support vector machine
	Nsengimana, Cyprien; Han, XinTong; Wang, HaiYu; Shen, Xiu Jun; Li, Lingling
通讯作者	Nsengimana, C (corresponding author), Hebei Univ Technol, Sch Elect Engn, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300130, Peoples R China.
会议名称	International Symposium on Computer, Consumer and Control (IS3C)
会议日期	NOV 13-16, 2020
会议地点	Natl Chin Yi Univ Technol, Taichung, TAIWAN
英文摘要	With the current increasing demand in energy consumption, there is a huge increase of prominent energy problems that require us to imperatively seek for the new green energy sources. Photovoltaic power generation is one of the most feasible power generation methods due to its high cleanliness and static characteristics. 'This paper proposes a photoelectric power prediction method based on an improved firefly algorithm to optimize support vector machines (SVM) for short-term prediction. We effectively combine the regression support vector machine (SVR) with the modified firefly algorithm (MFFA) and use the firefly estimation method to determine the best fitness penalty factor c and kernel function g, so that the support vector machine can better predict the photovoltaic power. In order to make the firefly algorithm to optimize the support vector machine faster, we improved the firefly algorithm step factor a and introduced a weight coefficient ca. Compared with conventional techniques, this method has better prediction results and prediction speed is also better than the traditional intelligent optimization models. Let's take the data from a photovoltaic base in the Desert Knowledge Australian Solar Energy Centre (DKASC) as an example.
英文关键词	short-term prediction support vector machine improved firefly algorithm prediction speed
来源出版物	2020 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C 2020)
ISSN	2476-1052
出版年	2021
页码	344-346
ISBN	978-1-7281-9362-5
出版者	IEEE
类型	Proceedings Paper
语种	英语
收录类别	CPCI-S
WOS记录号	WOS:000669743300089
WOS关键词	NEURAL-NETWORK ; PREDICTION
WOS类目	Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/365655
作者单位	[Nsengimana, Cyprien; Han, XinTong; Wang, HaiYu; Shen, Xiu Jun; Li, Lingling] Hebei Univ Technol, Sch Elect Engn, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300130, Peoples R China
推荐引用方式 GB/T 7714	Nsengimana, Cyprien,Han, XinTong,Wang, HaiYu,et al. Short-term Photovoltaic Power Forecasting Based on Improved Firefly Algorithm to optimize support vector machine[C]:IEEE,2021:344-346.