上海用电负荷预测中气温累积效应分析
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摘要
用电负荷预测是电力运行管理工作的基础,随着"互联网+"智慧能源发展,由人工预测转信息化系统自动预测为必然趋势。文中选日最高温度为负荷预测气象特征,考虑气温累积效应,提出预测日最高气温修正法,并分析负荷与气温的相关性。最后给出基于决策树模型预测下一工作日最高用电负荷的算法。
Power load forecast is essential in government management on electric power operation.With the rapid development of"Internet+"smart energy,it is a trend to substitute artificial load forecast into automatic forecast based on information system.In this paper,daily highest temperature is chosen as weather characteristic in prediction model.Considering temperature accumulation effect,revision of highest temperature on prediction day is presented,and correlation of power load and temperature is analyzed.In the end,aprediction method of highest power load of next work day is given based on decision tree model.
Power load forecast is essential in government management on electric power operation.With the rapid development of"Internet+"smart energy,it is a trend to substitute artificial load forecast into automatic forecast based on information system.In this paper,daily highest temperature is chosen as weather characteristic in prediction model.Considering temperature accumulation effect,revision of highest temperature on prediction day is presented,and correlation of power load and temperature is analyzed.In the end,aprediction method of highest power load of next work day is given based on decision tree model.
引文
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[2]康重庆,夏清,刘梅.电力系统负荷预测[M].2版.北京:中国电力出版社,2017.
[3]华蕴芳.上海气温变化对用电负荷的影响[J].上海节能,2007(2):14-15.
[4]贺芳芳,史军.上海地区夏季气温变化对用电负荷的影响[J].长江流域资源与环境,2011,20(12):1462-1467.
[5]王鹏,邰能灵,王波,等.针对气象因素的短期负荷预测修正方法[J].电力系统自动化,2008,32(13):92-96.
[6]忻建华,翟海青,葛佳男,等.上海市用电负荷与温度敏感系数[J].华东电力,2012,40(10):1754-1758.
[7]上海市经济和信息化委员会.全社会用电量.[2017-5-7].www.datashanghai.gov.cn(上海市政府数据服务网).
[8]盛骤,谢式千,潘承毅.概率论与数理统计[M].3版.北京:高等教育出版社,2002.
[9]姚春艳,赵寅珠,王双副.统计学[M].哈尔滨:哈尔滨工业大学出版社,2015.
[10]周志华.机器学习[M].北京:清华大学出版社,2016.
[11]Wu X,Kumar V,Ross Quinlan J,et al.Top 10algorithms in data mining[J].Knowledge and Information Systems,2008,14(1):1-37.
[12]Breiman L,Friedman J H,Olshen R A,et al.Classification and regression trees[M].Wadsworth,Belmont,1984.
[13]Quinlan J R.C4.5:Programs for machine learning[M].Morgan Kaufmann,San Mateo,CA.,1993.