电力大数据下的短期电力负荷预测
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摘要
电力产业是国民工业系统中重要的产业。在电网运行管理中,对于负荷预测具有非常重要的作用。更加准确的电力负荷预测可以为电网的安全稳定运行、实时进行电网负荷的调度提供了重要依据。特别是在经济方面,精确的电力负荷预测可以优化发、用电电网调度计划,合理调度和分配资源,从而起到使社会效益、经济效益最大化的作用。然而随着中国经济的飞速发展,对电力的需求不断增长,电力负荷本身受诸多因素以及政策影响比如日期、天气、气候、市场等其他因素,这些因素更大大加大了准确进行电力负荷预测的困难性。一直以来,人们一直都致力于提高电力负荷预测的准确性,人工神经网络算法具有泛化、学习能力强等优点,现在该算法已在电力负荷预测领域中得到了广泛应用,并且取得了良好的效果。近年来,人工神经网络领域取得重大突破,涌现出一个新的深度学习研究领域。文章就是基于最新发展的人工神经网络算法,结合实际地区电网数据研究了短期电力负荷预测的相关问题。
The power industry plays a pillar role in the national industrial system. The smooth operation of electricity is related to the lifeline of the national economy. In power system management,power load forecasting is crucial. Accurate power load forecasting can provide important basis for the smooth operation of the system and real-time power dispatching. Especially in the economic field,power load forecasting can play a significant role in rationally deploying resources,optimizing power generation plans,and achieving optimal social and economic benefits. However,with the rapid economic development of our country,the demand for electricity is increasing day by day,and the power load itself is also affected by the date,weather,climate,market,and policies,which greatly increases the difficulty of accurately predicting the power load. People have always been committed to improving the accuracy of power load forecasting. Artificial neural networks have the advantages of self-learning and generalization ability. They have been widely used in power load forecasting and have achieved satisfactory results. In recent years,the field of artificial neural networks has made gratifying breakthroughs,and a new research field of deep learning has emerged. This article based on the latest development of artificial neural network,combined with the actual data on the short-term powerload forecasting issues related research.
The power industry plays a pillar role in the national industrial system. The smooth operation of electricity is related to the lifeline of the national economy. In power system management,power load forecasting is crucial. Accurate power load forecasting can provide important basis for the smooth operation of the system and real-time power dispatching. Especially in the economic field,power load forecasting can play a significant role in rationally deploying resources,optimizing power generation plans,and achieving optimal social and economic benefits. However,with the rapid economic development of our country,the demand for electricity is increasing day by day,and the power load itself is also affected by the date,weather,climate,market,and policies,which greatly increases the difficulty of accurately predicting the power load. People have always been committed to improving the accuracy of power load forecasting. Artificial neural networks have the advantages of self-learning and generalization ability. They have been widely used in power load forecasting and have achieved satisfactory results. In recent years,the field of artificial neural networks has made gratifying breakthroughs,and a new research field of deep learning has emerged. This article based on the latest development of artificial neural network,combined with the actual data on the short-term powerload forecasting issues related research.
引文
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[12]朱晓露.基于BP神经网络的电力负荷中长期预测[D].北京:华北电力大学,2017.
[13]程宇也.基于人工神经网络的短期电力负荷预测研究[D].杭州:浙江大学.2017.
[14]王志勇.数据挖掘方法在短期负荷预测中的应用研究[D].杭州:浙江大学.2007.
[15]杜莉.张建军.神经网络在电力负荷预测中的应用研究[J].计算机仿真.2011,10(3):297-300.DU Li.ZHANG Jianjun.research on application of neural network in power load forecasting[J].Computer Simulation,2011,10(3):297-300.
[16]夏博,杨超,李冲.电力系统短期负荷预测方法研究综述[J].电力大数据,2018,(7):22-28.XIA Bo,YANG Chao,LI Chong.Summary of short-term load forecasting methods for power systems[J].Electric Power Big Data,2018(7):22-28.
[2]JAMES A.MILLER,CRAIG T.BOWMAN.Mechanism and modeling of nitrogen chemistry in combustion[J].Progress in Energy and Combustion Science.2013.
[3]魏宗儒.简析电力负荷预测对发展电力市场的作用[J].甘肃科技,2013,17(12):88-90.WEI Zongru.Analysis of the effect of power load forecasting on the development of electricity market[J].Gansu Science and Technology,2013,17(12):88-90.
[4]胡长洪.考虑实时气象因素的短期负荷预测[D].杭州:浙江大学,2010.
[5]徐凯.沈阳市配电网中长期负荷预测[D].北京:华北电力大学,2014.
[6]JIA Y.LI S.TAN Y.et al.Improved parametric estimation of logistic model for saturated load forecast[J].Power&Energy Engineering Conference,2012:1-4.
[7]康重庆.夏清.刘梅.电力系统负荷预测[M].北京:中国电力出版社,2007.
[8]王硕.毛西吟.短期电力负荷预测分析研究[J].科技风.2014.(1):13-14.WANG Shuo.MAO Xiyin.Analysis of short-term power load forecast analysis[J].Science&Technology Style,2014.(1):13-14.
[9]潘雪涛.基于改进BP神经网络算法的短期负荷预测[J].上海电力学院学报.2012.28(4):388-391.PAN Xuetao.Short-term load forecasting based on improved BPneural network algorithm[J].Journal of Shanghai University of Electric Power,2012.28(4):388-391.
[10]雷水平.王超胜.崔景顺.短期电力负荷预测影响因素分析与研究[J].硅谷.2014.(21):220-220.LEI Shuiping.WANG Chaosheng.CUI Jingshun.Analysis and research on influence factors of short-term power load forecasting[J].Silicon Valley,2014(21):220-220.
[11]刘冰.郭海霞.MATLAB神经网络超级学习手册[M].北京:人民邮电出版社.2013.
[12]朱晓露.基于BP神经网络的电力负荷中长期预测[D].北京:华北电力大学,2017.
[13]程宇也.基于人工神经网络的短期电力负荷预测研究[D].杭州:浙江大学.2017.
[14]王志勇.数据挖掘方法在短期负荷预测中的应用研究[D].杭州:浙江大学.2007.
[15]杜莉.张建军.神经网络在电力负荷预测中的应用研究[J].计算机仿真.2011,10(3):297-300.DU Li.ZHANG Jianjun.research on application of neural network in power load forecasting[J].Computer Simulation,2011,10(3):297-300.
[16]夏博,杨超,李冲.电力系统短期负荷预测方法研究综述[J].电力大数据,2018,(7):22-28.XIA Bo,YANG Chao,LI Chong.Summary of short-term load forecasting methods for power systems[J].Electric Power Big Data,2018(7):22-28.