Short-term load forecasting of long-short term memory neural network based on genetic algorithm
文献类型：会议论文
| 作者 | 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
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| 出版日期 | 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
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| 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 |
| 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.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences 4.Shenyang Institute of Automation, Chinese Academy of Sciences Beijing, China 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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