基于云理论和层次分析法的城市电力空间负荷预测
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摘要
本文提出一种基于云理论和层次分析法的城市电力空间负荷预测方法。空间负荷预测是城市电网规划的基础,其核心问题是负荷指标的选取。负荷指标受多方面不确定因素的影响,因而选取精确合理的负荷指标成为一个较为复杂的决策问题。
在大量的数据调研和分析的基础上,本文选取了对小区负荷指标起到关键性作用的影响因素作为负荷指标优选的依据。首先建立云发生器以及云推理规则,将定性的影响因素量化,计算影响因素的客观权重；其次建立层次分析模型,依据专家经验给出的判断矩阵,计算影响因素的主观权重；再次结合主、客观权重计算出综合权重；最后基于样本数据,利用综合权重,预测小区最优负荷指标以及小区负荷,并采用负荷曲线叠加的方法得到地区总负荷。
本文所提出的综合权重在考虑多个影响因素的前提下,能够兼顾客观规律性和专家经验。模型建立在大量样本数据的基础上,通过建立云发生器将有限的样本数据进行扩展,从而克服了样本数据难以收集的缺陷。以某年的影响因素值作为模型的输入,可以得到该年的负荷分布情况。因此,本文模型可以方便地预测负荷的空间分布,而且不受时间限制。
以某地区的实际数据对本文模型进行验证,结果表明本文提出的预测模型具有一定的精度和可操作性,在空间负荷预测中具有一定的优越性。
This paper presents a spatial load forecasting method based on cloud theory and analytic hierarchy process. Spatial load forecasting is the basis of distribution network planning, and it hinges on choosing suitable load density. Because load density is influenced by many uncertain factors, how to select the suitable load density becomes a complicated decision problem.
Based on large amounts of data investigated, this paper selects the influencing factors which play important roles in the load density. Firstly, establish cloud generators and inference rules to quantize the qualitative factors, and then calculate the objective weights of the influencing factors. Secondly, establish hierarchical analysis models, form judgment matrixes according to the experience of experts and calculate the subjective weights of influencing factors. Thirdly, calculate synthetical weights based on objective weights and subjective weights. Finally, using the synthetical weights to forecast the optimal load density and the value of load based on sample data and then take the superimposition curve as the total load curve of the predicted area.
The synthetical weights proposed in this paper can take account of the objective regularity and the experience of experts on the premise of considering many influencing factors. The selecting of load density bases on a large number of sample data. This paper expands limited sample data by cloud generators so that overcomes the difficulty of getting sample data. Inputting the values of influencing factors in a given year of the area to be predicted, the model can work out the load density of the area in the given year. This method can not only predict the spatial distribution of the electric power load, but also predict the evolution process of the spatial load.
An example demonstrates the superiority of the proposed model in spatial load forecasting.
在大量的数据调研和分析的基础上,本文选取了对小区负荷指标起到关键性作用的影响因素作为负荷指标优选的依据。首先建立云发生器以及云推理规则,将定性的影响因素量化,计算影响因素的客观权重；其次建立层次分析模型,依据专家经验给出的判断矩阵,计算影响因素的主观权重；再次结合主、客观权重计算出综合权重；最后基于样本数据,利用综合权重,预测小区最优负荷指标以及小区负荷,并采用负荷曲线叠加的方法得到地区总负荷。
本文所提出的综合权重在考虑多个影响因素的前提下,能够兼顾客观规律性和专家经验。模型建立在大量样本数据的基础上,通过建立云发生器将有限的样本数据进行扩展,从而克服了样本数据难以收集的缺陷。以某年的影响因素值作为模型的输入,可以得到该年的负荷分布情况。因此,本文模型可以方便地预测负荷的空间分布,而且不受时间限制。
以某地区的实际数据对本文模型进行验证,结果表明本文提出的预测模型具有一定的精度和可操作性,在空间负荷预测中具有一定的优越性。
This paper presents a spatial load forecasting method based on cloud theory and analytic hierarchy process. Spatial load forecasting is the basis of distribution network planning, and it hinges on choosing suitable load density. Because load density is influenced by many uncertain factors, how to select the suitable load density becomes a complicated decision problem.
Based on large amounts of data investigated, this paper selects the influencing factors which play important roles in the load density. Firstly, establish cloud generators and inference rules to quantize the qualitative factors, and then calculate the objective weights of the influencing factors. Secondly, establish hierarchical analysis models, form judgment matrixes according to the experience of experts and calculate the subjective weights of influencing factors. Thirdly, calculate synthetical weights based on objective weights and subjective weights. Finally, using the synthetical weights to forecast the optimal load density and the value of load based on sample data and then take the superimposition curve as the total load curve of the predicted area.
The synthetical weights proposed in this paper can take account of the objective regularity and the experience of experts on the premise of considering many influencing factors. The selecting of load density bases on a large number of sample data. This paper expands limited sample data by cloud generators so that overcomes the difficulty of getting sample data. Inputting the values of influencing factors in a given year of the area to be predicted, the model can work out the load density of the area in the given year. This method can not only predict the spatial distribution of the electric power load, but also predict the evolution process of the spatial load.
An example demonstrates the superiority of the proposed model in spatial load forecasting.
引文
[1]Q/GDW 156-2006,城市电力网规划设计导则[S].北京国家电网公司,2006
[2]Willis H.-L. Power Distribution Planning Reference Book[M]. New York:Marcel Dekker Inc,1997:15-50
[3]Palayanon, et al. Computerized Distribution Load Forecast for the City of Bangkok[J]. IEEE Trans on Power Systems,1987, V2(1):1250-1257
[4]Padmakumari, S.K., Mohandas, et al. Application of fuzzy system theory in land use based long term distribution load forecasting[A]. Proceedings of Energy Management and Power Delivery[C],1998:311-316
[5]Blagajac S, Krajcar S, Skrlec D. Computational tool to support spatial electric load forecasting[A]. Electrotechnical Conference[C],2000,3:1116-1119
[6]Kab Ju Hwang, Gwang Won Kin. Spatial load forecasting model for electrical distribution planning[J]. Science and Technology. KORUS 2004 Proceedings the 8th Russian-Korean International Symposium,2004,1:237-241
[7]Willis H.-L., J.E.D., Northcote-Green. Spatial electric load forecasting:a tutorial review[J]. Proc. The IEEE,1983,71:232-253
[8]Xueming Yang, Jinsha Yuan, et al. Application of Uncertainty Reasoning Based on Cloud Theory in Spatial Load Forecasting[A]. Intelligent Control and Automation [C].2006,2:7567-7571
[9]Chow, M.-Y., Tram, H. Application of fuzzy logic technology for spatial load forecasting[J]. IEEE Transactions on Power Systems,1997,12(3):1360-1366
[10]Chow, M.-Y., Tram, H. Methodology of urban re-development considerations in spatial load forecasting[J]. IEEE Transactions on Power Systems,1997,12(2): 996-1001
[11]吴蓓,丁明,陈闽江.基于模糊推理和多目标规划的空间负荷预测[J].电网技术,2004,28(15)：48-52
[12]余贻鑫,张弘鹏,张崇见等.空间电力负荷小区用地分析的模糊推理新方法[J].天津大学学报,2002,35(2)：135-139
[13]Wu H.-C., Lu C.-N. A data mining approach for spatial modeling in small area load forecast[J]. IEEE Transactions on Power Systems,2002,17(2):516-521
[14]雷绍兰,孙才新等.模糊粗糙集理论在空间电力负荷预测中的应用[J].电网技术,2005,29(9)：26-30
[15]王天华,王平洋,范明天.遗传算法、模糊逻辑和运输模型在配电网空间负荷预测中的应用[J].电网技术,1999,23(1)：24-28
[16]周湶,李健,孙才新等.基于粗糙集和元胞自动机的配电网空间负荷预测[J].中国电机工程学报,2008,28(25)：68-73
[17]李建云.基于负荷密度模型的长沙城区负荷预测[J].中外建筑,2005：46-48
[18]Fu Yang, Liu Lujie, Zhu Lan, Cao Jialin. Research on new method to select load density based on fuzzy comprehensive evaluation[A]. Electric Utility Deregulation and Restructuring and Power Technologies[C].2008:1384-1387
[19]陶文斌,张粒子,潘弘等.基于双层贝叶斯分类的空间负荷预测[J].中国电机 工程学报,2007,27(7)：13-17
[20]Li De-yi, Han J, Chan E, et al. Knowledge representation and discovery based on linguistic atoms[J]. Proc the 1st Pacific Asia Conf KDD & DM, Singapore,1997: 1-6
[21]林丽清ROUGH集和云理论在空间数据挖掘中的应用[D].北京：北京化工大学,2007
[22]刘常昱,李德毅,韩旭.正态云模型的统计分析[J].计算机科学,2005,34(4)：236-240
[23]Fang Wang, Yanpeng Li, Xiang Li. Performance evaluation for automatic target recognition based on cloud theory[A].2008 international conference on radar[C]. 2008:498-502
[24]Cao Yang, Song Weidong. Evaluation Model of Rubber Planting Suitability Based on Cloud Theory, Rough Set and Fuzzy Neural Network[A].2009 WRI Global Congress on Intelligent Systems[C].2009,1:456-459
[25]Gao Hongyan, Liu Fasheng. Identification method of traffic state based on cloud theory[A]. Advanced Computer Control[C].2010,2:182-186
[26]Qiuyu Zhang, Lei Sun, Yanfeng Jin. Description of Key Performance Index States based on Cloud Theory and Weight Mean method for Multi-sensor[A]. Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing[C].2007,1:350-354
[27]Qiu Lin, Chen Xiaonan, Chen Haitao, et al. Evaluation model of water resources reproducible ability in Yellow River basin based on cloud theory[A]. Natural Computation[C].2010,8:3966-3969
[28]Ji He, Chen Xiaonan, Qiu Lin. Assessment model based on cloud theory for water resources carrying capacity[A]. Education Technology and Computer[C].2010,4: 135-138
[29]王玲.基于云理论的配电网络规划研究[D].河北：华北电力大学,2009
[30]董军,陈小良,肖霖等.基于云理论的输电投资项目实物期权评价研究[J].华东电力,2009,37(2)：217-221
[31]温秀峰.基于云理论的电力系统运行风险评估的研究[D].河北：华北电力大学,2009
[32]Yang, X.-M., Yuan, J.-S., Mao, H.-N.. A Novel Cloud Theory Based Time-series Predictive Method for Middle-term Electric Load Forecasting[A]. Computational Engineering in Systems Applications[C].2006:1920-1924
[33]孙秋野,张化光,王云爽.基于组合云发生器的配电系统负荷模糊建模[J].系统仿真学报,2008,20(2)：341-344
[34]Hu Shiyuan, Li Deren, Liu Yaolin, et al. Mining Weights of Land Evaluation Factors Based on Cloud Model and Correlation Analysis[J]. Geo-Spatial Information Science,2007,10(3):218-222
[35]杜鷁等.一种测试数据挖掘算法的数据源生成方法[J].计算机研究与发展,2000,37(7)：776-782
[36]刘耀林.土地信息系统[M].北京：中国农业出版社,2003：11-15
[37]徐晓敏.层次分析法的运用[J].统计与决策,2008,1：156-158
[38]Zhao Wei, Wang Yan-wen. Research on Compre Hpensive Appraisement of Branchis of the Industry to the National Economy[J]. Proceeding of Apors', O.R.S. of China,1992:1-6
[39]Yingchen Wang, Yonghao Sun, Xiaoxiao Geng, et al. Research of AHP Model on Combination between the Social Responsibility and the Economic Benefits in Corporation[A]. Business and Information Management[C].2008,2:449-452
[40]Shang Hai-yang, Su Fang. Evaluation for Urban Sustainable Development Based on AHP[A]. Intelligent Information Technology Application Workshops[C],2009: 38-41
[41]郭新志,乞建勋,李星梅.基于改进模糊层次分析法的火电厂选址综合评价方法研究[J].现代电力,2007,24(3)：67-71
[42]董张卓,焦建林,孙启宏.用层次分析法安排电力系统事故后火电机组恢复的次序[J].电网技术,1997,21(6)：48-51
[43]Huang Yi, Huang Wen-jie. Analysis of the Life Cycle Cost of the Power Plants Based on Analytic Hierarchy Process Method[A]. Information Management, Innovation Management and Industrial Engineering[C].2009,4:639-642
[44]Fattahi, S., Afsharnia, S., Javidi, M.H.. A new AHP-Based reactive power valuation method[A]. Electric Power Conference[C].2008:1-7
[45]Jun Yang, Xin Ai, Tao Zhao. An evidential reasoning approach to risk assessments in composite generation and transmission system[A]. Electric Utility Deregulation and Restructuring and Power Technologies[C].2008:323-328
[46]Shanshan Wang, Guangfu Tang, Zhiyuan He. Comprehensive evaluation of VSC-HVDC transmission based on improved analytic hierarchy process[A]. Electric Utility Deregulation and Restructuring and Power Technologies[C].2008: 2207-2211
[47]Liu Zi-fa, Zhang Jie-chao, Li Cong. Spatial electric load forecasting based on AHP and TOPSIS method[A]. Advances in Power System Control, Operation and Management[C].2009:1-6
[48]朱学文,王庭喜,熊浩清.基于模糊区间层次分析法的相对标度电能质量评估应用[J].电气应用,2008,27(19)：56-60
[49]鞠平.电力系统负荷建模理论与实践[J].电力系统自动化,1999,23(19)：1-7
[50]赵希正.中国电力负荷特性分析与预测[M].北京：中国电力出版社,2001：151-176
[51]乌鲁木齐统计年鉴[M].北京：中国统计出版社,2003：253
[52]陈成忠,林振山.中国居民消费价格指数波动的周期性及其驱动因素研究[J].经济问题探索,2009,8：77-84
[2]Willis H.-L. Power Distribution Planning Reference Book[M]. New York:Marcel Dekker Inc,1997:15-50
[3]Palayanon, et al. Computerized Distribution Load Forecast for the City of Bangkok[J]. IEEE Trans on Power Systems,1987, V2(1):1250-1257
[4]Padmakumari, S.K., Mohandas, et al. Application of fuzzy system theory in land use based long term distribution load forecasting[A]. Proceedings of Energy Management and Power Delivery[C],1998:311-316
[5]Blagajac S, Krajcar S, Skrlec D. Computational tool to support spatial electric load forecasting[A]. Electrotechnical Conference[C],2000,3:1116-1119
[6]Kab Ju Hwang, Gwang Won Kin. Spatial load forecasting model for electrical distribution planning[J]. Science and Technology. KORUS 2004 Proceedings the 8th Russian-Korean International Symposium,2004,1:237-241
[7]Willis H.-L., J.E.D., Northcote-Green. Spatial electric load forecasting:a tutorial review[J]. Proc. The IEEE,1983,71:232-253
[8]Xueming Yang, Jinsha Yuan, et al. Application of Uncertainty Reasoning Based on Cloud Theory in Spatial Load Forecasting[A]. Intelligent Control and Automation [C].2006,2:7567-7571
[9]Chow, M.-Y., Tram, H. Application of fuzzy logic technology for spatial load forecasting[J]. IEEE Transactions on Power Systems,1997,12(3):1360-1366
[10]Chow, M.-Y., Tram, H. Methodology of urban re-development considerations in spatial load forecasting[J]. IEEE Transactions on Power Systems,1997,12(2): 996-1001
[11]吴蓓,丁明,陈闽江.基于模糊推理和多目标规划的空间负荷预测[J].电网技术,2004,28(15)：48-52
[12]余贻鑫,张弘鹏,张崇见等.空间电力负荷小区用地分析的模糊推理新方法[J].天津大学学报,2002,35(2)：135-139
[13]Wu H.-C., Lu C.-N. A data mining approach for spatial modeling in small area load forecast[J]. IEEE Transactions on Power Systems,2002,17(2):516-521
[14]雷绍兰,孙才新等.模糊粗糙集理论在空间电力负荷预测中的应用[J].电网技术,2005,29(9)：26-30
[15]王天华,王平洋,范明天.遗传算法、模糊逻辑和运输模型在配电网空间负荷预测中的应用[J].电网技术,1999,23(1)：24-28
[16]周湶,李健,孙才新等.基于粗糙集和元胞自动机的配电网空间负荷预测[J].中国电机工程学报,2008,28(25)：68-73
[17]李建云.基于负荷密度模型的长沙城区负荷预测[J].中外建筑,2005：46-48
[18]Fu Yang, Liu Lujie, Zhu Lan, Cao Jialin. Research on new method to select load density based on fuzzy comprehensive evaluation[A]. Electric Utility Deregulation and Restructuring and Power Technologies[C].2008:1384-1387
[19]陶文斌,张粒子,潘弘等.基于双层贝叶斯分类的空间负荷预测[J].中国电机 工程学报,2007,27(7)：13-17
[20]Li De-yi, Han J, Chan E, et al. Knowledge representation and discovery based on linguistic atoms[J]. Proc the 1st Pacific Asia Conf KDD & DM, Singapore,1997: 1-6
[21]林丽清ROUGH集和云理论在空间数据挖掘中的应用[D].北京：北京化工大学,2007
[22]刘常昱,李德毅,韩旭.正态云模型的统计分析[J].计算机科学,2005,34(4)：236-240
[23]Fang Wang, Yanpeng Li, Xiang Li. Performance evaluation for automatic target recognition based on cloud theory[A].2008 international conference on radar[C]. 2008:498-502
[24]Cao Yang, Song Weidong. Evaluation Model of Rubber Planting Suitability Based on Cloud Theory, Rough Set and Fuzzy Neural Network[A].2009 WRI Global Congress on Intelligent Systems[C].2009,1:456-459
[25]Gao Hongyan, Liu Fasheng. Identification method of traffic state based on cloud theory[A]. Advanced Computer Control[C].2010,2:182-186
[26]Qiuyu Zhang, Lei Sun, Yanfeng Jin. Description of Key Performance Index States based on Cloud Theory and Weight Mean method for Multi-sensor[A]. Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing[C].2007,1:350-354
[27]Qiu Lin, Chen Xiaonan, Chen Haitao, et al. Evaluation model of water resources reproducible ability in Yellow River basin based on cloud theory[A]. Natural Computation[C].2010,8:3966-3969
[28]Ji He, Chen Xiaonan, Qiu Lin. Assessment model based on cloud theory for water resources carrying capacity[A]. Education Technology and Computer[C].2010,4: 135-138
[29]王玲.基于云理论的配电网络规划研究[D].河北：华北电力大学,2009
[30]董军,陈小良,肖霖等.基于云理论的输电投资项目实物期权评价研究[J].华东电力,2009,37(2)：217-221
[31]温秀峰.基于云理论的电力系统运行风险评估的研究[D].河北：华北电力大学,2009
[32]Yang, X.-M., Yuan, J.-S., Mao, H.-N.. A Novel Cloud Theory Based Time-series Predictive Method for Middle-term Electric Load Forecasting[A]. Computational Engineering in Systems Applications[C].2006:1920-1924
[33]孙秋野,张化光,王云爽.基于组合云发生器的配电系统负荷模糊建模[J].系统仿真学报,2008,20(2)：341-344
[34]Hu Shiyuan, Li Deren, Liu Yaolin, et al. Mining Weights of Land Evaluation Factors Based on Cloud Model and Correlation Analysis[J]. Geo-Spatial Information Science,2007,10(3):218-222
[35]杜鷁等.一种测试数据挖掘算法的数据源生成方法[J].计算机研究与发展,2000,37(7)：776-782
[36]刘耀林.土地信息系统[M].北京：中国农业出版社,2003：11-15
[37]徐晓敏.层次分析法的运用[J].统计与决策,2008,1：156-158
[38]Zhao Wei, Wang Yan-wen. Research on Compre Hpensive Appraisement of Branchis of the Industry to the National Economy[J]. Proceeding of Apors', O.R.S. of China,1992:1-6
[39]Yingchen Wang, Yonghao Sun, Xiaoxiao Geng, et al. Research of AHP Model on Combination between the Social Responsibility and the Economic Benefits in Corporation[A]. Business and Information Management[C].2008,2:449-452
[40]Shang Hai-yang, Su Fang. Evaluation for Urban Sustainable Development Based on AHP[A]. Intelligent Information Technology Application Workshops[C],2009: 38-41
[41]郭新志,乞建勋,李星梅.基于改进模糊层次分析法的火电厂选址综合评价方法研究[J].现代电力,2007,24(3)：67-71
[42]董张卓,焦建林,孙启宏.用层次分析法安排电力系统事故后火电机组恢复的次序[J].电网技术,1997,21(6)：48-51
[43]Huang Yi, Huang Wen-jie. Analysis of the Life Cycle Cost of the Power Plants Based on Analytic Hierarchy Process Method[A]. Information Management, Innovation Management and Industrial Engineering[C].2009,4:639-642
[44]Fattahi, S., Afsharnia, S., Javidi, M.H.. A new AHP-Based reactive power valuation method[A]. Electric Power Conference[C].2008:1-7
[45]Jun Yang, Xin Ai, Tao Zhao. An evidential reasoning approach to risk assessments in composite generation and transmission system[A]. Electric Utility Deregulation and Restructuring and Power Technologies[C].2008:323-328
[46]Shanshan Wang, Guangfu Tang, Zhiyuan He. Comprehensive evaluation of VSC-HVDC transmission based on improved analytic hierarchy process[A]. Electric Utility Deregulation and Restructuring and Power Technologies[C].2008: 2207-2211
[47]Liu Zi-fa, Zhang Jie-chao, Li Cong. Spatial electric load forecasting based on AHP and TOPSIS method[A]. Advances in Power System Control, Operation and Management[C].2009:1-6
[48]朱学文,王庭喜,熊浩清.基于模糊区间层次分析法的相对标度电能质量评估应用[J].电气应用,2008,27(19)：56-60
[49]鞠平.电力系统负荷建模理论与实践[J].电力系统自动化,1999,23(19)：1-7
[50]赵希正.中国电力负荷特性分析与预测[M].北京：中国电力出版社,2001：151-176
[51]乌鲁木齐统计年鉴[M].北京：中国统计出版社,2003：253
[52]陈成忠,林振山.中国居民消费价格指数波动的周期性及其驱动因素研究[J].经济问题探索,2009,8：77-84