章姝俊,陆海清,陈佳玺,邵越
ZHANG Shujun,LU Haiqing,CHEN Jiaxi,SHAO Yue

• 1:国网浙江省电力有限公司
• 2:国网浙江省电力有限公司经济技术研究院
• 3:国网浙江省电力有限公司湖州供电公司

摘要(Abstract)：

在全球变暖的大背景下，气候的不稳定性给电力系统的安全运行带来了挑战，科学准确预测采暖、降温等气温敏感负荷的用电需求显得尤为重要。传统电力系统气温敏感负荷预测方法容易忽略气候、地理及社会等多方面因素的综合影响，对此提出一种基于“3T”（温度、区域、时间）模型的降温/采暖负荷及电量增长预测方法。在实现气温敏感负荷及电量分解的基础上，首先梳理影响降温/采暖用电的气温因素及社会因素。接着，基于历史数据，选取与气温敏感负荷相关性较大的影响因素，并借助所提优化模型确定负荷和电量预测函数。然后，在函数中代入相应时段的气温及社会情况预测数据，得到预测结果。最后通过算例验证了该方法的有效性，结果表明，在气温敏感负荷预测中考虑多因素可以使测算结果更贴近实际，能更好地适应未来温度变化趋势对用电需求的影响。
Amidst the backdrop of global warming, the unstable climate presents challenges to the secure operation of power systems. Therefore, it is particularly crucial to accurately and scientifically forecast the electricity demand of temperature-sensitive loads such as heating and cooling. Conventional methods for predicting temperaturesensitive load fail to encompass the comprehensive influence of factors such as climate, geography, and society.Therefore, a method of forecasting the increase of cooling and heating loads and electricity based on the 3T(temperature, territory and time) model. Based on the decomposition of temperature-sensitive load and electricity, the temperature and social factors affecting electricity consumption for cooling and heating are first sorted out. Then, the influencing factors with greater correlation with the temperature-sensitive load are selected based on historical data, and the load and electricity forecasting function is determined with the help of the proposed optimization model.Then, the predicted data of temperature and social conditions of the corresponding time period are substituted in the function to obtain the prediction results. Finally, the efficacy of the method is validated through case studies. Results indicate that considering multiple factors in temperature-sensitive load prediction yields estimations closer to reality, enhancing the adaptation of electricity demand to future temperature trends.

关键词(KeyWords)： 气温敏感负荷;相关性分析;用电需求;预测;气温因素;社会因素
temperature-sensitive load;correlation analysis;electricity demand;forecasting;temperature factors;social factors

Abstract:

Keywords:

基金项目(Foundation): 国家电网有限公司科技项目（5700-202257453A-2-0-ZN）

作者(Author): 章姝俊,陆海清,陈佳玺,邵越
ZHANG Shujun,LU Haiqing,CHEN Jiaxi,SHAO Yue

DOI: 10.19585/j.zjdl.202309004

参考文献(References)：

• [1]林卫斌，陈彬，俞燕山.“十二五”及2020年电力需求预测研究[J].中国人口资源与环境，2011,21(7):1-6.LIN Weibin,CHEN Bin,YU Yanshan.Research on forecasting electricity demand of the 12th five-year and 2020[J].China Population,Resources and Environment,2011,21(7):1-6.
• [2]刘希颖.中国电力需求预测与电力行业可持续发展[D].厦门：厦门大学，2009.LIU Xiying.Electricity demand forecasting and sustainable development of electricity industry in China[D].Xiamen:Xiamen University,2009.
• [3]徐阳.基于经济新常态的城市电网电量需求预测研究[D].北京：华北电力大学，2017.XU Yang. Electricity demand forecast of city power grid based on the new economic normal[D]. Beijing:North China Electric Power University,2017.
• [4] HUO L W,WANG J,JIN D C,et al.Increased summer electric power demand in Beijing driven by preceding spring tropical North Atlantic warming[J]. Atmospheric and Ocean Science Letters,2022,15(1):6.
• [5]朱斌，李扬，刘一丹，等.江苏省2003年夏季气温对电力负荷的影响[J].江苏电机工程，2004,23(2):12-14.ZHU Bin,LI Yang,LIU Yidan,et al.The effect of summer atmospheric temperature to electric power load of Jiangsu Province in the year 2003[J].Jiangsu Electrical Engineering,2004,23(2):12-14.
• [6]魏炎初，蒋建波.电力需求预测与经济发展的相关性[J].广西大学学报（哲学社会科学版），2010,32（增刊1）:23-25.WEI Yanchu,JIANG Jianbo. Correlation between power demand forecast and economic development[J].Journal of Guangxi University(Philosophy and Social Science),2010,32(S1):23-25.
• [7]钟全辉，张以全，肖少华，等.基于灰色预测理论的区域电量概率预测方法及其应用[J].浙江电力，2018,37(1):19-22.ZHONG Quanhui,ZHANG Yiquan,XIAO Shaohua,et al. Probability forecasting method of regional electricity quantity based on grey forecasting theory and its application[J].Zhejiang Electric Power,2018,37(1):19-22.
• [8]苏宜靖，谷炜，赵依，等.考虑气象因子的区域电网梅雨期负荷预测[J].浙江电力，2019,38(12):1-7.SU Yijing,GU Wei,ZHAO Yi,et al.Load forecasting of regional power grid during the plum rains considering meteorological factors[J].Zhejiang Electric Power,2019,38(12):1-7.
• [9]曹敏，李文云，钱详华，等.基于分类识别深度置信网络的电力负荷预测算法[J].电力需求侧管理，2020,22(2):44-50.CAO Min,LI Wenyun,QIAN Xianghua,et al.Power load forecasting algorithm based on classified identification deep belief network[J]. Power Demand Side Management,2020,22(2):44-50.
• [10]王晓红，吴德会.一种基于最小二乘支持向量机的年电力需求预测方法[J].继电器，2006(16):74-78.WANG X H,WU D. Annual electric consumption forecasting model based on least square support vector machines[J].Relay,2006(16):74-78.
• [11]余洋，权丽，贾雨龙，等.平抑新能源功率波动的聚合温控负荷改进模型预测控制[J].电力自动化设备，2021,41(3):92-99.YU Yang,QUAN Li,JIA Yulong,et al.Improved model predictive control of aggregated thermostatically controlled load for power fluctuation suppression of new energy[J].Electric Power Automation Equipment,2021,41(3):92-99.
• [12]史静，周琪，谈健，等.江苏电网夏季空调负荷特性挖掘与温度敏感性辨识[J].电力工程技术，2018,37(3):28-32.SHI Jing,ZHOU Qi,TAN Jian,et al.The load excavation and temperature sensitivity identification of air conditioning in summer of Jiangsu power grid[J]. Electric Power Engineering Technology,2018,37(3):28-32.
• [13]王德文，孙志伟.电力用户侧大数据分析与并行负荷预测[J].中国电机工程学报，2015,35(3):527-537.WANG Dewen,SUN Zhiwei.Big data analysis and parallel load forecasting of electric power user side[J].Proceedings of the CSEE,2015,35(3):527-537.
• [14]何华琴，何后裕.基于WTVS模型的电力系统短期负荷预测[J].计算机与数字工程，2019,47(7):1571-1575.HE Huaqin,HE Houyu. Short-term load forecasting of power system based on WTVS model[J]. Computer&Digital Engineering,2019,47(7):1571-1575.
• [15]章剑光，刘理峰，林海峰，等.基于空间相似度和深度学习的中长期用电量预测[J].浙江电力，2021,40(5):45-52.ZHANG Jianguang,LIU Lifeng,LIN Haifeng,et al.Medium and long-term electricity consumption prediction based on spatial similarity and deep learning[J].Zhejiang Electric Power,2021,40(5):45-52.
• [16]谭风雷，丁心志，张军，等.基于变化趋势和气象因子的加权负荷预测方法[J].电力需求侧管理，2022,24(3):66-72.TAN Fenglei,DING Xinzhi,ZHANG Jun,et al.Weighted load forecasting method based on change trend and meteorological factors[J]. Power Demand Side Management,2022,24(3):66-72.
• [17]胡怡霜，夏翔，丁一，等.基于因子和趋势分析反馈的多元回归负荷预测[J].电力需求侧管理，2018,20(6):22-25.HU Yishuang,XIA Xiang,DING Yi,et al.A multivariate regression load forecasting algorithm based on factor and tendency analysis feedback[J].Power Demand Side Management,2018,20(6):22-25.
• [18]屠一艳，徐久益，杨晓雷，等.计及经济因素的随机森林电量预测[J].浙江电力，2021,40(3):91-96.TU Yiyan,XU Jiuyi,YANG Xiaolei,et al.Electricity consumption forecasting under random forests considering economic factors[J]. Zhejiang Electric Power,2021,40(3):91-96.
• [19]李闯，孔祥玉，朱石剑，等.能源互联环境下考虑需求响应的区域电网短期负荷预测[J].电力系统自动化，2021,45(1):71-78.LI Chuang,KONG Xiangyu,ZHU Shijian,et al. Shortterm load forecasting of regional power grid considering demand response in energy interconnection environment[J].Automation of Electric Power Systems,2021,45(1):71-78.
• [20]庞传军，张波，余建明.基于LSTM循环神经网络的短期电力负荷预测[J].电力工程技术，2021,40(1):175-180.PANG Chuanjun,ZHANG Bo,YU Jianming.Short-term power load forecasting based on LSTM recurrent neural network[J]. Electric Power Engineering Technology,2021,40(1):175-180.
• [21]丁恰，张辉，张君毅.考虑气象信息的节假日负荷预测[J].电力系统自动化，2005,29(17):93-97.DING Qia,ZHANG Hui,ZHANG Junyi. Temperature sensitive method for short term load forecasting during holidays[J].Automation of Electric Power Systems,2005,29(17):93-97.
• [22]杨星磊，项川，姜鸣瞻，等.计及产业结构和温度因素的神经网络电网负荷预测方法[J].电力电容器与无功补偿，2022,43(6):79-84.YANG Xinglei,XIANG Chuan,JIANG Mingzhan,et al.Neural network load estimation method considering industrial structure and temperature factors[J].Power Capacitor&Reactive Power Compensation,2022,43(6):79-84.
• [23]贺莉微，任永建.不同时间尺度下气象因子对电网负荷预测的影响[J].水电能源科学，2020,38(9):206-210.HE Liwei,REN Yongjian.Effect of meteorological factors on electric power load forecasting at different time scales[J].Water Resources and Power,2020,38(9):206-210.
• [24]陈章潮，熊岗.应用改进的灰色GM(1,1)模型进行长期电力需求预测[J].电力系统自动化，1993,17(7):20-24.CHEN Zhangchao,XIONG Gang. Long-term load forecasting for Pudong new area of Shanghai using grey theory[J].Automation of Electric Power Systems,1993,17(7):20-24.
• [25]刘炬，刘闯，徐达，等.基于综合气象指数的EA-SNN组合负荷预测模型[J].山东电力技术，2022,49(8):10-14.LIU Ju,LIU Chuang,XU Da,et al. EA-SNN combined load forecasting model based on comprehensive meteorological index[J].Shandong Electric Power,2022,49(8):10-14.
• [26]黄海萍.基于BP神经网络的中国电力需求预测[J].科学技术与工程，2007,7(4):612-613.HUANG Haiping. Combination estimate for electric demand of China[J].Science Technology and Engineering,2007,7(4):612-613.