| 基于高阶BP神经网络的日最大负荷预测 |
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| 引用本文： | 马立新,李渊,郑晓栋,尹晶晶.基于高阶BP神经网络的日最大负荷预测[J].能源研究与信息,2016,32(3):153-157. |
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| 作者姓名： | 马立新 李渊 郑晓栋 尹晶晶 |
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| 作者单位： | 上海理工大学 光电信息与计算机工程学院, 上海 200093,上海理工大学 光电信息与计算机工程学院, 上海 200093,上海理工大学 光电信息与计算机工程学院, 上海 200093,上海理工大学 光电信息与计算机工程学院, 上海 200093 |
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| 基金项目： | 国家自然科学基金资助项目（61205076） |
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| 摘 要： | 随着社会的发展,人们的日常生活和工作生产越来越依赖于电力系统.精准的电力负荷预测是电网安全、稳定运行的重要保障.为减小节假日在日最大负荷预测过程中的影响,提出了法定节假日对日最大负荷的影响及日类型量化处理方法,并采用一种改进的BP(back propagation)神经网络——高阶BP神经网络进行连续多天最大负荷预测.实验算例结果表明:该数据处理和预测方法能有效地减小节假日对负荷预测的影响,提高了预测精度,并有较强的工程实践价值和应用前景.
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| 关 键 词： | 负荷预测 日最大负荷 日类型 假日负荷预测 高阶BP神经网络 |
| 收稿时间： | 2014/9/19 0:00:00 |
Daily peak load prediction based on advanced BP neural network |
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| MA Lixin,LI Yuan,ZHENG Xiaodong and YIN Jingjing.Daily peak load prediction based on advanced BP neural network[J].Energy Research and Information,2016,32(3):153-157. |
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| Authors: | MA Lixin LI Yuan ZHENG Xiaodong and YIN Jingjing |
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| Affiliation: | School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China,School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China,School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China and School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China |
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| Abstract: | With the development of society,people''s daily life and work increasingly depend on the production of power system.Accurate power load prediction is a key guarantee for safe and stable operation of the grid.Based on the characteristics of historical load data,effect of legal holiday on the daily peak load as well as the method of quantifying day type were put forward in this paper,when the daily peak load was predicted.A modified back propagation(BP) neural network,advanced BP network(ABP),was adopted to predict the continuous multi-day daily peak load.The simulation results showed that this data processing and prediction method could effectively demonstrated the influence of holiday on load prediction and improved the prediction accuracy.It has a strong practice value and wide application prospect. |
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| Keywords: | load forecast daily peak load day type holiday load forecast advanced BP neural network |
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