Short-term load forecasting of long-short term memory neural network based on genetic algorithm

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业控制网络与系统研究室

	Short-term load forecasting of long-short term memory neural network based on genetic algorithm
	Li WT(李婉婷)1,2,3,4,5; Zang CZ(臧传治)1,2,3,4; Liu D(刘鼎)1,2,3,4,5; Zeng P(曾鹏)1,2,3,4
	2020
会议日期	October 30 - November 1, 2020
会议地点	Wuhan, China
关键词	load forecasting long-short term neural networks genetic algorithm learning rate iteration number
页码	2518-2522
英文摘要	Accurate load forecasting is conducive to the reasonable arrangement of power grid dispatching plans. Traditional load forecasting methods cannot handle the time series and nonlinear characteristics of load well. Long-short term memory (LSTM) neural networks can record long-term and short-term information, which can effectively solve this kind of problem. But the parameters of LSTM network are difficult to determine. For this reason, this paper proposes a long-short term neural network based on genetic algorithm. The learning rate and iteration number of the LSTM network are used as chromosomes, and the genes are continuously selected, crossed, and mutated to obtain more good genes. Comparing this method with the standard LSTM network, the simulation results show that the LSTM network using genetic algorithm for parameter optimization improves the prediction accuracy of the standard LSTM network by 63%.
源文献作者	Beijing Sifang Automation Co., Ltd. ; Dongfang Electronics Co., Ltd ; et al. ; Journal of Huadian Technology ; South China Intelligent Electrical Technology Co., Ltd ; Sunwoda Electronic Co., Ltd
产权排序	1
会议录	2020 IEEE 4th Conference on Energy Internet and Energy System Integration: Connecting the Grids Towards a Low-Carbon High-Efficiency Energy System, EI2 2020
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-7281-9606-0
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/28360]
专题	沈阳自动化研究所_工业控制网络与系统研究室
通讯作者	Zang CZ(臧传治)
作者单位	1.Shenyang Institute of Automation, Chinese Academy of Sciences Beijing, China 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences 5.University of Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Li WT,Zang CZ,Liu D,et al. Short-term load forecasting of long-short term memory neural network based on genetic algorithm[C]. 见:. Wuhan, China. October 30 - November 1, 2020.