考虑大气污染防治措施影响的短期电力负荷预测模型研究
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摘要
2013年以来,我国出台了一系列大气污染防治措施,旨在降低大气污染水平,这些措施的实施会通过影响电力用户用电行为来影响地区的短期用电需求。分析了大气污染防治措施对地区短期用电需求的影响机理,构建了考虑大气污染防治措施影响的短期负荷预测模型,以北京市为例进行了实证分析,指出大气污染防治措施对北京市短期负荷具有显著影响。此外,所构建的基于K均值聚类和SSA-LSSVM方法的预测模型具有计算复杂度低、能够避免模型参数设定的主观性等特点,为短期负荷预测提供了有效工具。
Since 2013, China has introduced a series of air pollution prevention and control policies aiming at reducing the air pollution.The implementation of these measures will affect the short-term electricity demand of the region by affecting the electricity consumption behavior of power users. The impact mechanism of air pollution prevention and control policy on regions' short-term electricity demand are analyzed,and a short-term load forecasting model is constructed considering the influence of these measures.The empirical analysis is carried out by taking Beijing as an example. It is found that air pollution prevention and control policy has a significant impact on short-term load in Beijing. In addition,the proposed forecasting model based on K-means clustering and SSALSSVM method has low computational complexity and can avoid the subjectivity of model parameter setting,which provides an effective tool for short-term load forecasting.
Since 2013, China has introduced a series of air pollution prevention and control policies aiming at reducing the air pollution.The implementation of these measures will affect the short-term electricity demand of the region by affecting the electricity consumption behavior of power users. The impact mechanism of air pollution prevention and control policy on regions' short-term electricity demand are analyzed,and a short-term load forecasting model is constructed considering the influence of these measures.The empirical analysis is carried out by taking Beijing as an example. It is found that air pollution prevention and control policy has a significant impact on short-term load in Beijing. In addition,the proposed forecasting model based on K-means clustering and SSALSSVM method has low computational complexity and can avoid the subjectivity of model parameter setting,which provides an effective tool for short-term load forecasting.
引文
[1]杨贺,刘金平.区域复合型大气污染时空扩散及其联防联控策略研究[J].现代商贸工业,2018,39(6):196-197.YANG He, LIU Jinping. Research on the time-spacediffusion and joint prevention and control strategy of regional compound air pollution[J].Modern Business Trade Industry, 2018,39(6):196-197.
[2]刘瑞瑞.空气污染对人群健康风险的统计分析[D].广州:暨南大学,2016.
[3]童光毅,王梦真.我国能源结构低碳化转型的有关问题分析与研究[J].智慧电力,2017,45(7):6-8.TONG Guangyi,WANG Mengzhen. Analysis&study on low-carbon transformation of energy structure[J]. Smart Power,2017,45(7):6-8.
[4]曹慧丰,毕巍强,曾诗鸿.产业结构调整的大气污染治理效应——以河北省为例[J].管理世界,2015(12):182-183.CAO Huifeng, BI Weiqiang, ZENG Shihong.The effect of air pollution control on industrial structure adjustment:acase study of Hebei Province[J]. Management World,2015(12):182-183.
[5]刘亚南,林其友,王骏,等.电力消费弹性系数与各产业关联度的研究[J].广东电力,2017,30(5):52-55.LIU Yanan, LIN Qiyou, WANG Jun, et al. Research on correlation between electricity elastic coefficient and corresponding industrial structure[J]. Guangdong Electric Power, 2017, 30(5):52-55.
[6]石磊,王玥,程荣,等.京津冀产业结构调整对大气污染物排放的影响效应——基于向量自回归(VAR)模型的脉冲响应函数分析[J].科技导报,2018,36(15):24-31.SHI Lei, WANG Yue, CHENG Rong, et al.Effects of industrial structure adjustment on air pollutant emission in Beijing-Tianjin-Hebei region based on the impulse response function of VAR model[J]. Science&Technology Review, 2018,36(15):24-31.
[7]冯贵霞.大气污染防治政策变迁与解释框架构建——基于政策网络的视角[J].中国行政管理,2014(9):16-20+80.FENG Guixia. Framework construction for the air pollution prevention and control policy change and interpretation:from a policy networks perspective[J].Chinese Public Administration, 2014(9):16-20+80.
[8]王金南,王慧丽,雷宇.京津冀及周边地区秋冬季大气污染防治重点及建议[J].环境保护,2017,45(21):12-16.WANG Jinnan, WANG Huili, LEI Yu. Key problems and prospects on air pollution control in fall and winter season in the great Beijing-Tianjin-Hebei region[J]. Environmental Protection, 2017,45(21):12-16.
[9] MI Z F, PAN S Y, YU H, et al. Potential impacts of industrial structure on energy consumption and CO_2emission:a case study of Beijing[J]. Journal of Cleaner Production, 2015, 103:455-462.
[10]袁家海,张文华.中国煤电过剩规模量化及去产能路径研究[J].中国能源,2017,39(8):14-20.YUAN Jiahai, ZHANG Wenhua. Research on the quantification and de-capacity path of China's coal-fired excess scale[J].Energy of China,2017, 39(8):14-20.
[11]袁家海,雷祺,王杨.经济新常态下中国电力需求展望及煤电前景分析[J].中国能源,2015(7):21-27.YUAN Jiahai, LEI Qi, WANG Yang. China's electricity demand outlook and coal power prospect analysis under the new economic normal[J]. Energy of China, 2015(7):21-27.
[12]孙毅,石墨,单葆国,等.基于粒子群优化支持向量机的电能替代潜力分析方法[J].电网技术,2017,41(6):1767-1771.SUN Yi, SHI Mo, SHAN Baoguo, et al. Electric energy substitution potential analysis method based on particle swarm optimization support vector machine[J]. Power System Technology, 2017,41(6):1767-1771.
[13]单葆国,孙祥栋,李江涛,等.经济新常态下中国电力需求增长研判[J].中国电力,2017,50(1):19-24.SHAN Baoguo, SUN Xiangdong, LI Jiangtao, et al.Analysis on the China's electricity demand growth under the new economic norm[J]. Electric Power, 2017,50(1):19-24.
[14]傅守强,汪莹,刘素伊,等.基于市场的中长期电力消费需求预测[J].电网与清洁能源,2015,31(7):74-80.FU Shouqiang, WANG Ying, LIU Suyi, et al.Forecasting medium and long term electricity requirement based on market[J]. Power System and Clean Energy, 2015,31(7):74-80.
[15]刘国静,谈健,李琥,等.基于Logistic模型的电能替代电量预测[J].电力工程技术,2018,37(6):39-43.LIU Guojing, TAN Jian, LI Hu, et al.Forecast of electric energy substitution based on logistic model[J]. Electric Power Engineering Technology,2018,37(6):39-43.
[16]凤凰资讯.我国几乎所有污染物指标排放量均排世界第一[E B/OL].http://news.ifeng.com/a/20161206/50368423_0.shtml,2016-12-06.
[17]杨颖,杨少华,张燕,等.基于相似日的短期电价区间预测[J].智慧电力,2018,46(12):23-29.YANG Ying, YANG Shaohua, ZHANG Yan, et al.Short-term electricity price forecasting based on similar day[J]. Smart Power,2018,46(12):23-27.
[18] HARTIGAN J A, WONG M A. Algorithm AS 136:a kmeans clustering algorithm[J]. Journal of the RoyalStatistical Society.Series C(Applied Statistics), 1979, 28(1):100-108.
[19] KANUNGO T, MOUNT D M, NETANYAHU N S, et al.An efficient k-means clustering algorithm:analysis and implementation[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence,2002,(7):881-892.
[20] CALINSKI T, HARABASZ J. A dendrite method for cluster analysis[J]. Communications in Statistics-theory and Methods, 1974,3(1):1-27.
[21]唐玮,钟士元,舒娇,等.基于GRA-LSSVM的配电网空间负荷预测方法研究[J].电力系统保护与控制,2018,46(24):76-82.TANG Wei, ZHONG Shiyuan, SHU Jiao, et al.Research on spatial load forecasting of distribution network based on GRA-LSSVM method[J]. Power System Protection and Control,2018,46(24):76-82.
[22]郭丽娟,孙世宇,段修生.支持向量机及核函数研究[J].科学技术与工程,2008,(2):487-490.GUO Lijuan, SUN Shiyu, DUAN Xiusheng. Research for support vector machine and kernel function[J]. Science Technology and Engineering,2008,(2):487-490.
[23] MIRJALILI S,GANDOMI A H, MIRJALILI S Z, et al.Salp swarm algorithm:a bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software,2017, 114:163-191.
[24]张平,潘学萍,薛文超.基于小波分解模糊灰色聚类和BP神经网络的短期负荷预测[J].电力自动化设备,2012,32(11):121-125+141.ZHANG Ping, PAN Xueping, XUE Wenchao.Short-term load forecasting based on wavelet decomposition, fuzzy gray correlation clustering and BP neural network[J].Electric Power Automation Equipment, 2012, 32(11):121-125+141.
[25]陶琳,岳小冰.一种新的粒子群算法优化支持向量机的短期负荷预测[J].电子设计工程,2016,24(16):151-154.TAO Lin, YUE Xiaobing. A new power load chaotic predicting based on support vector machine and particleswarm optimization algorithm[J].Electronic Design Engineering,2016,24(16):151-154.
[2]刘瑞瑞.空气污染对人群健康风险的统计分析[D].广州:暨南大学,2016.
[3]童光毅,王梦真.我国能源结构低碳化转型的有关问题分析与研究[J].智慧电力,2017,45(7):6-8.TONG Guangyi,WANG Mengzhen. Analysis&study on low-carbon transformation of energy structure[J]. Smart Power,2017,45(7):6-8.
[4]曹慧丰,毕巍强,曾诗鸿.产业结构调整的大气污染治理效应——以河北省为例[J].管理世界,2015(12):182-183.CAO Huifeng, BI Weiqiang, ZENG Shihong.The effect of air pollution control on industrial structure adjustment:acase study of Hebei Province[J]. Management World,2015(12):182-183.
[5]刘亚南,林其友,王骏,等.电力消费弹性系数与各产业关联度的研究[J].广东电力,2017,30(5):52-55.LIU Yanan, LIN Qiyou, WANG Jun, et al. Research on correlation between electricity elastic coefficient and corresponding industrial structure[J]. Guangdong Electric Power, 2017, 30(5):52-55.
[6]石磊,王玥,程荣,等.京津冀产业结构调整对大气污染物排放的影响效应——基于向量自回归(VAR)模型的脉冲响应函数分析[J].科技导报,2018,36(15):24-31.SHI Lei, WANG Yue, CHENG Rong, et al.Effects of industrial structure adjustment on air pollutant emission in Beijing-Tianjin-Hebei region based on the impulse response function of VAR model[J]. Science&Technology Review, 2018,36(15):24-31.
[7]冯贵霞.大气污染防治政策变迁与解释框架构建——基于政策网络的视角[J].中国行政管理,2014(9):16-20+80.FENG Guixia. Framework construction for the air pollution prevention and control policy change and interpretation:from a policy networks perspective[J].Chinese Public Administration, 2014(9):16-20+80.
[8]王金南,王慧丽,雷宇.京津冀及周边地区秋冬季大气污染防治重点及建议[J].环境保护,2017,45(21):12-16.WANG Jinnan, WANG Huili, LEI Yu. Key problems and prospects on air pollution control in fall and winter season in the great Beijing-Tianjin-Hebei region[J]. Environmental Protection, 2017,45(21):12-16.
[9] MI Z F, PAN S Y, YU H, et al. Potential impacts of industrial structure on energy consumption and CO_2emission:a case study of Beijing[J]. Journal of Cleaner Production, 2015, 103:455-462.
[10]袁家海,张文华.中国煤电过剩规模量化及去产能路径研究[J].中国能源,2017,39(8):14-20.YUAN Jiahai, ZHANG Wenhua. Research on the quantification and de-capacity path of China's coal-fired excess scale[J].Energy of China,2017, 39(8):14-20.
[11]袁家海,雷祺,王杨.经济新常态下中国电力需求展望及煤电前景分析[J].中国能源,2015(7):21-27.YUAN Jiahai, LEI Qi, WANG Yang. China's electricity demand outlook and coal power prospect analysis under the new economic normal[J]. Energy of China, 2015(7):21-27.
[12]孙毅,石墨,单葆国,等.基于粒子群优化支持向量机的电能替代潜力分析方法[J].电网技术,2017,41(6):1767-1771.SUN Yi, SHI Mo, SHAN Baoguo, et al. Electric energy substitution potential analysis method based on particle swarm optimization support vector machine[J]. Power System Technology, 2017,41(6):1767-1771.
[13]单葆国,孙祥栋,李江涛,等.经济新常态下中国电力需求增长研判[J].中国电力,2017,50(1):19-24.SHAN Baoguo, SUN Xiangdong, LI Jiangtao, et al.Analysis on the China's electricity demand growth under the new economic norm[J]. Electric Power, 2017,50(1):19-24.
[14]傅守强,汪莹,刘素伊,等.基于市场的中长期电力消费需求预测[J].电网与清洁能源,2015,31(7):74-80.FU Shouqiang, WANG Ying, LIU Suyi, et al.Forecasting medium and long term electricity requirement based on market[J]. Power System and Clean Energy, 2015,31(7):74-80.
[15]刘国静,谈健,李琥,等.基于Logistic模型的电能替代电量预测[J].电力工程技术,2018,37(6):39-43.LIU Guojing, TAN Jian, LI Hu, et al.Forecast of electric energy substitution based on logistic model[J]. Electric Power Engineering Technology,2018,37(6):39-43.
[16]凤凰资讯.我国几乎所有污染物指标排放量均排世界第一[E B/OL].http://news.ifeng.com/a/20161206/50368423_0.shtml,2016-12-06.
[17]杨颖,杨少华,张燕,等.基于相似日的短期电价区间预测[J].智慧电力,2018,46(12):23-29.YANG Ying, YANG Shaohua, ZHANG Yan, et al.Short-term electricity price forecasting based on similar day[J]. Smart Power,2018,46(12):23-27.
[18] HARTIGAN J A, WONG M A. Algorithm AS 136:a kmeans clustering algorithm[J]. Journal of the RoyalStatistical Society.Series C(Applied Statistics), 1979, 28(1):100-108.
[19] KANUNGO T, MOUNT D M, NETANYAHU N S, et al.An efficient k-means clustering algorithm:analysis and implementation[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence,2002,(7):881-892.
[20] CALINSKI T, HARABASZ J. A dendrite method for cluster analysis[J]. Communications in Statistics-theory and Methods, 1974,3(1):1-27.
[21]唐玮,钟士元,舒娇,等.基于GRA-LSSVM的配电网空间负荷预测方法研究[J].电力系统保护与控制,2018,46(24):76-82.TANG Wei, ZHONG Shiyuan, SHU Jiao, et al.Research on spatial load forecasting of distribution network based on GRA-LSSVM method[J]. Power System Protection and Control,2018,46(24):76-82.
[22]郭丽娟,孙世宇,段修生.支持向量机及核函数研究[J].科学技术与工程,2008,(2):487-490.GUO Lijuan, SUN Shiyu, DUAN Xiusheng. Research for support vector machine and kernel function[J]. Science Technology and Engineering,2008,(2):487-490.
[23] MIRJALILI S,GANDOMI A H, MIRJALILI S Z, et al.Salp swarm algorithm:a bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software,2017, 114:163-191.
[24]张平,潘学萍,薛文超.基于小波分解模糊灰色聚类和BP神经网络的短期负荷预测[J].电力自动化设备,2012,32(11):121-125+141.ZHANG Ping, PAN Xueping, XUE Wenchao.Short-term load forecasting based on wavelet decomposition, fuzzy gray correlation clustering and BP neural network[J].Electric Power Automation Equipment, 2012, 32(11):121-125+141.
[25]陶琳,岳小冰.一种新的粒子群算法优化支持向量机的短期负荷预测[J].电子设计工程,2016,24(16):151-154.TAO Lin, YUE Xiaobing. A new power load chaotic predicting based on support vector machine and particleswarm optimization algorithm[J].Electronic Design Engineering,2016,24(16):151-154.
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