基于混沌与改进BP神经网络的电价预测
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摘要
电力市场中电价是关系到资源流动和利益分配的核心因素,电价的确定也是电力市场中最本质、最关键的部分,根据市场需求确定相应的合理电价直接影响到电力市场能否正常的运营。
电价预测具有十分重要的意义,目前已提出了时间序列法、人工神经网络、混沌理论等预测方法,但由于电价预测不仅需要考虑电价本身作为时间序列的特性,还需要考虑各种非电价因素(如负荷需求、节假日、供求关系等因素)对电价的影响,特别是人为因素的干扰,这都进一步加大了电价预测的难度和深度。
混沌理论己被证明是解决非线性问题的重要的、行之有效的理论方法。混沌动力系统的奇异吸引子所具有的无穷自相似性使混沌理论和分形学自然紧密联系。基于混沌理论的非线性时间序列分析跳出了传统的建立主观模型的局限,通过时间序列的内在规律做出分析与预测。
本文在综合考虑影响电价的各种因素下,以电价时间序列的混沌特性为基础,利用多变量时间序列的相空间重构理论并结合人工神经网络的非线性映射能力建立数学模型,提出一种基于Lyapunov指数的混沌神经网络电价短期预测方法,该方法简单、方便,能够较为准确地预测电力交易价格,预测电力交易价格。使用本方法对美国加州实际的系统边际价格进行预测,得到了较好的结果,表明了本方法的有效性。
Electricity Price issues are the key problems in the markets and how to price the special commodity-electricity is essential for the smooth market operation. So using the relative historic data in predicting the future electricity Price is a very meaningful work.
The neural network technique has gained high recognition in recent twenty years and has acquired abundant accomplishment. BP feed-forward networks can be applied to nonlinear modeling, function pattern association and pattern classification. As to actual problem, there is no system method to solve network architecture and neurons, quite a few experiments must be made. The paper introduces the way to create a network, train a network and simulate a network with MATLAB language. Some cautions are introduced too.
Chaotic theory has been proved to be an important and useful theory algorithm. The natural tightly was connected between chaos and Fractal due to the infinite similarities of strange attractor of chaotic dynamic system. Nonlinear time series analysis based on chaotic theory cross through traditional frame of subjective model,draw out Prediction on the inner rules of chaotic time series data.
This thesis advances a short-term price forecasting method based on Lyapunov exponent after comprehensively analyzes the relative factors. The method combines with the chaos theory and artificial neural networks and presents an improved BP neural networks model Based on chaotic analysis in the phases pace. Testing on California’s electricity market proves this method’s efficiency.
电价预测具有十分重要的意义,目前已提出了时间序列法、人工神经网络、混沌理论等预测方法,但由于电价预测不仅需要考虑电价本身作为时间序列的特性,还需要考虑各种非电价因素(如负荷需求、节假日、供求关系等因素)对电价的影响,特别是人为因素的干扰,这都进一步加大了电价预测的难度和深度。
混沌理论己被证明是解决非线性问题的重要的、行之有效的理论方法。混沌动力系统的奇异吸引子所具有的无穷自相似性使混沌理论和分形学自然紧密联系。基于混沌理论的非线性时间序列分析跳出了传统的建立主观模型的局限,通过时间序列的内在规律做出分析与预测。
本文在综合考虑影响电价的各种因素下,以电价时间序列的混沌特性为基础,利用多变量时间序列的相空间重构理论并结合人工神经网络的非线性映射能力建立数学模型,提出一种基于Lyapunov指数的混沌神经网络电价短期预测方法,该方法简单、方便,能够较为准确地预测电力交易价格,预测电力交易价格。使用本方法对美国加州实际的系统边际价格进行预测,得到了较好的结果,表明了本方法的有效性。
Electricity Price issues are the key problems in the markets and how to price the special commodity-electricity is essential for the smooth market operation. So using the relative historic data in predicting the future electricity Price is a very meaningful work.
The neural network technique has gained high recognition in recent twenty years and has acquired abundant accomplishment. BP feed-forward networks can be applied to nonlinear modeling, function pattern association and pattern classification. As to actual problem, there is no system method to solve network architecture and neurons, quite a few experiments must be made. The paper introduces the way to create a network, train a network and simulate a network with MATLAB language. Some cautions are introduced too.
Chaotic theory has been proved to be an important and useful theory algorithm. The natural tightly was connected between chaos and Fractal due to the infinite similarities of strange attractor of chaotic dynamic system. Nonlinear time series analysis based on chaotic theory cross through traditional frame of subjective model,draw out Prediction on the inner rules of chaotic time series data.
This thesis advances a short-term price forecasting method based on Lyapunov exponent after comprehensively analyzes the relative factors. The method combines with the chaos theory and artificial neural networks and presents an improved BP neural networks model Based on chaotic analysis in the phases pace. Testing on California’s electricity market proves this method’s efficiency.
引文
[1]苏娟,杜松怀,周兴华.电力市场现货电价预测方法研究状况综述.继电器, 2005,21(3): 25~27
[2]陈思杰,周浩.电力市场电价预测方法综述.继电器,2006,6(11): 15~17
[3] Wang A, Ramsay B. Prediction of System Marginal Price in the UK Power Pal Using Neural Networks. Restructuring and Power Technologies, 1997, 6(11): 2116~2120
[4]鄢玉,杨洁明.BP神经网络算法探讨.科技情报开发与经济,2006,8(3): 4~6
[5]谢培元,曾次玲.基于神经网络的短期电价预测.湖南电力, 2004,16(2): 26~29
[6] Qiaolin Ding, Jing Tang, Jianxin Liu. Application of new FCMAC neural network in power system marginal price forecasting Power Engineering Conference. Restructuring and Power Technologies, 2005, 26(1): 1352~1360
[7] Hu, Z.Yu, Y.; Wang, Z. Sun, W. Gan, D. Han, Z.Price forecasting using an integrated approach Electric Utility Deregulation. Journal of Restructuring and Power Technologies.2004, 26(1): 28~30
[8] Zhou, M, Yan, Z, Ni, Y.X, Li, G., Nie, Y.Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach Generation, Transmission and Distribution. Electrical Power and Power Systems, 2006, 153(2): 187 ~195
[9] Ming Zhou, Zheng Yan, Yixin Ni, Gengyin Li.An ARIMA approach to forecasting electricity price with accuracy improvement by predicted errors Power Engineering Society General Meeting. Restructuring and Power Technologies, 2004, 24(3): 233 ~238
[10] Conejo, A.J, Plazas, M.A, Espinola, R, Molina, A.B.Day-ahead electricity price forecasting using the wavelet transform and ARIMA models Power Systems, Electrical Power and Power Systems, 2005,20(2): 1035 ~1042
[11] Contreras, J, Espinola, R,Nogales, F.J, Conejo, A.J. ARIMA models to predict next-day electricity prices Power Systems. Electrical Power and Power Systems ,2003,18(3): 1014~1020
[12]赵庆波,周原冰,郭蓉,曾鸣.模糊神经网络在电力系统边际电价预测中的应用.电网技术, 2004,21(7): 15~17
[13] Amjady, N.Day-ahead price forecasting of electricity markets by a new fuzzy neural network Power Systems.Power Engineering, 2006,21(2): 887~896
[14]周明,聂艳丽,李庚银,倪以信.基于小波分析的短期电价ARIMA预测方法.电网技术, 2005,24(9): 35~37
[15] Conejo A.J,Plazas M.A,Espinola R, Molina A.B.Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models. Power Systems, 2005, 20(2):1035~1042
[16]程晓鑫,周渝慧.基于灰色改进模型的电价预测.华北电力大学学报, 2006,23(11): 23~25
[17]马欲,侯志俭,蒋传文,潘力强.电力远期价格预测的灰色动态模型研究.继电器, 2005,26(6): 13~15
[18]杨朴,游大海,谢培元.基于遗传算法和BP神经网络的电价预测.水电能源科学, 2003,4(2): 6~8
[19]李郁侠,赵军科,段凌剑,高福荣.混沌理论和快速BP神经网络在边际电价预测中的应用.武汉大学学报(工学版) ,2005,9(6): 12~14
[20]赵庆波,周原冰,郭蓉,曾鸣.模糊神经网络在电力系统边际电价预测中的应用.电网技术, 2004,14(7): 17~19
[21]孙国明,刘洪杰.一种基于动态模糊系统的发电商上网电价预测模型与算法.华北水利水电学院学报, 2004,18(4): 7~9
[22]聂艳丽,周明,杨照芬,李庚银.最优组合预测方法在电价预测中的应用.电力科学与工程, 2005,17(4): 3~6
[23]陆洋,忻建华,叶春.基于ARMAX模型的短期出清电价预测方法.华东电力, 2005,13(4): 14~16
[24]李眉眉,丁晶,覃光华.基于混沌分析的BP神经网络模型及其在负荷预测中的应用.四川大学学报(工程科学版) .2004,9(4): 4~6
[25] Zheng Hua,Xie Li, Zhang Li-zi. Electricity price forecasting based on GARCH model in deregulated market Power Engineering Conference. Power Engineering, 2005,23(2):1245~1248
[26] Zhengjun Liu, Hongming Yang, Mingyong Lai. Electricity price forecasting model based on chaos theory. Power Engineering ,2005,17(3): 342 ~348
[27] Nogales, F.J,Contreras, J, Conejo, A.J,Espinola, R.Forecasting next-day electricity prices by time series models Power Systems. Electrical Power and Power Systems.2002,17(3): 342 ~348
[28] Hongming Yang, Xianzhong Duan.Chaotic Characteristics of Electricity Price and Its Forecasting Model. Restructuring and Power Technologies, 2003, 17(2): 1237 ~1243.
[29]徐丽娜.神经网络控制].北京:电子工业出版社,2003
[30]常晓丽.基于Matlab的BP神经网络设计.机械工程与自动化,2006,16(4): 12~14
[31]蒲春,孙政顺.赵世敏Matlab神经网络工具箱BP算法比较,计算机仿真, 2006,13(5): 31~33
[32]朱斌,张国忠.一种基于MATLAB的混沌神经元动态.仿真与分析,2005,18(5): 12~14
[33]陈士华,陆君安.混沌动力学初步.武汉:武汉水利大学出版社,1998
[34]安鸿志.时间序列分析.上海:华东师范大学出版社,1990
[35]张本样.非线性的概念、性质及其哲学意义.自然辩证法研究, 1999,26(2): 34~35
[36]尚玲玲,汪全,刘广建.基于人工神经网络的短期系统边际电价预测.山东电力技术, 2006,14(4): 11~13
[37] Yamin H Y ,Shahidehpour S M.Li Z. A dative electricity short-term electricity price forecasting using artificial neural networks in the restructured power market. Electrical Power and Power Systems, 2004, 26 (2): 571~581
[38]修春波,刘向东,张宇河,唐运虞.一种新的混沌神经网络及其应用.电子学, 2005,5(5): 5~7
[39]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉大学出版社,2002.
[40]杨洪明,段献忠.电价的混沌特性分析及其预测模型研究.电网技术,2004,23(3): 17~18
[41]黄润生.混沌及其应用.武汉:武汉人学出版社.2000
[42]简相超,郑君里.混沌神经网络预测算法评价准则与性能分析清华大学学报(自然科学版), 2001,15(7): 25~27
[43]魏平,李均利.陈刚.基于小波分析的改进神经网络MCP预测方法及应用.电力系统自动化, 2004,18(11):19~21
[44] Mohammad Shahideh Pour,Hatim ralnin,Zuyi Li.Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management. Electrical Power and Power Systems 2002, 65(1): 1~21
[2]陈思杰,周浩.电力市场电价预测方法综述.继电器,2006,6(11): 15~17
[3] Wang A, Ramsay B. Prediction of System Marginal Price in the UK Power Pal Using Neural Networks. Restructuring and Power Technologies, 1997, 6(11): 2116~2120
[4]鄢玉,杨洁明.BP神经网络算法探讨.科技情报开发与经济,2006,8(3): 4~6
[5]谢培元,曾次玲.基于神经网络的短期电价预测.湖南电力, 2004,16(2): 26~29
[6] Qiaolin Ding, Jing Tang, Jianxin Liu. Application of new FCMAC neural network in power system marginal price forecasting Power Engineering Conference. Restructuring and Power Technologies, 2005, 26(1): 1352~1360
[7] Hu, Z.Yu, Y.; Wang, Z. Sun, W. Gan, D. Han, Z.Price forecasting using an integrated approach Electric Utility Deregulation. Journal of Restructuring and Power Technologies.2004, 26(1): 28~30
[8] Zhou, M, Yan, Z, Ni, Y.X, Li, G., Nie, Y.Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach Generation, Transmission and Distribution. Electrical Power and Power Systems, 2006, 153(2): 187 ~195
[9] Ming Zhou, Zheng Yan, Yixin Ni, Gengyin Li.An ARIMA approach to forecasting electricity price with accuracy improvement by predicted errors Power Engineering Society General Meeting. Restructuring and Power Technologies, 2004, 24(3): 233 ~238
[10] Conejo, A.J, Plazas, M.A, Espinola, R, Molina, A.B.Day-ahead electricity price forecasting using the wavelet transform and ARIMA models Power Systems, Electrical Power and Power Systems, 2005,20(2): 1035 ~1042
[11] Contreras, J, Espinola, R,Nogales, F.J, Conejo, A.J. ARIMA models to predict next-day electricity prices Power Systems. Electrical Power and Power Systems ,2003,18(3): 1014~1020
[12]赵庆波,周原冰,郭蓉,曾鸣.模糊神经网络在电力系统边际电价预测中的应用.电网技术, 2004,21(7): 15~17
[13] Amjady, N.Day-ahead price forecasting of electricity markets by a new fuzzy neural network Power Systems.Power Engineering, 2006,21(2): 887~896
[14]周明,聂艳丽,李庚银,倪以信.基于小波分析的短期电价ARIMA预测方法.电网技术, 2005,24(9): 35~37
[15] Conejo A.J,Plazas M.A,Espinola R, Molina A.B.Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models. Power Systems, 2005, 20(2):1035~1042
[16]程晓鑫,周渝慧.基于灰色改进模型的电价预测.华北电力大学学报, 2006,23(11): 23~25
[17]马欲,侯志俭,蒋传文,潘力强.电力远期价格预测的灰色动态模型研究.继电器, 2005,26(6): 13~15
[18]杨朴,游大海,谢培元.基于遗传算法和BP神经网络的电价预测.水电能源科学, 2003,4(2): 6~8
[19]李郁侠,赵军科,段凌剑,高福荣.混沌理论和快速BP神经网络在边际电价预测中的应用.武汉大学学报(工学版) ,2005,9(6): 12~14
[20]赵庆波,周原冰,郭蓉,曾鸣.模糊神经网络在电力系统边际电价预测中的应用.电网技术, 2004,14(7): 17~19
[21]孙国明,刘洪杰.一种基于动态模糊系统的发电商上网电价预测模型与算法.华北水利水电学院学报, 2004,18(4): 7~9
[22]聂艳丽,周明,杨照芬,李庚银.最优组合预测方法在电价预测中的应用.电力科学与工程, 2005,17(4): 3~6
[23]陆洋,忻建华,叶春.基于ARMAX模型的短期出清电价预测方法.华东电力, 2005,13(4): 14~16
[24]李眉眉,丁晶,覃光华.基于混沌分析的BP神经网络模型及其在负荷预测中的应用.四川大学学报(工程科学版) .2004,9(4): 4~6
[25] Zheng Hua,Xie Li, Zhang Li-zi. Electricity price forecasting based on GARCH model in deregulated market Power Engineering Conference. Power Engineering, 2005,23(2):1245~1248
[26] Zhengjun Liu, Hongming Yang, Mingyong Lai. Electricity price forecasting model based on chaos theory. Power Engineering ,2005,17(3): 342 ~348
[27] Nogales, F.J,Contreras, J, Conejo, A.J,Espinola, R.Forecasting next-day electricity prices by time series models Power Systems. Electrical Power and Power Systems.2002,17(3): 342 ~348
[28] Hongming Yang, Xianzhong Duan.Chaotic Characteristics of Electricity Price and Its Forecasting Model. Restructuring and Power Technologies, 2003, 17(2): 1237 ~1243.
[29]徐丽娜.神经网络控制].北京:电子工业出版社,2003
[30]常晓丽.基于Matlab的BP神经网络设计.机械工程与自动化,2006,16(4): 12~14
[31]蒲春,孙政顺.赵世敏Matlab神经网络工具箱BP算法比较,计算机仿真, 2006,13(5): 31~33
[32]朱斌,张国忠.一种基于MATLAB的混沌神经元动态.仿真与分析,2005,18(5): 12~14
[33]陈士华,陆君安.混沌动力学初步.武汉:武汉水利大学出版社,1998
[34]安鸿志.时间序列分析.上海:华东师范大学出版社,1990
[35]张本样.非线性的概念、性质及其哲学意义.自然辩证法研究, 1999,26(2): 34~35
[36]尚玲玲,汪全,刘广建.基于人工神经网络的短期系统边际电价预测.山东电力技术, 2006,14(4): 11~13
[37] Yamin H Y ,Shahidehpour S M.Li Z. A dative electricity short-term electricity price forecasting using artificial neural networks in the restructured power market. Electrical Power and Power Systems, 2004, 26 (2): 571~581
[38]修春波,刘向东,张宇河,唐运虞.一种新的混沌神经网络及其应用.电子学, 2005,5(5): 5~7
[39]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉大学出版社,2002.
[40]杨洪明,段献忠.电价的混沌特性分析及其预测模型研究.电网技术,2004,23(3): 17~18
[41]黄润生.混沌及其应用.武汉:武汉人学出版社.2000
[42]简相超,郑君里.混沌神经网络预测算法评价准则与性能分析清华大学学报(自然科学版), 2001,15(7): 25~27
[43]魏平,李均利.陈刚.基于小波分析的改进神经网络MCP预测方法及应用.电力系统自动化, 2004,18(11):19~21
[44] Mohammad Shahideh Pour,Hatim ralnin,Zuyi Li.Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management. Electrical Power and Power Systems 2002, 65(1): 1~21
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