基于模糊控制与RBF-NN的电动汽车充电站短期负荷预测模型研究
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摘要
电动汽车大量的充电负荷会冲击当地电网,为提高电动汽车充电站的负荷预测精度,提出了基于模糊控制在线修正RBF-NN短期负荷预测模型。该预测模型采用模糊控制原理对RBF-NN短期负荷预测模型的结果进行在线修正,与单一RBF-NN短期负荷预测模型相比,精度有了进一步的提高,证明了该预测模型的优越性。
A large number of electric vehicle charging loads will impact the local power grid. In order to improve the load forecasting accuracy of electric vehicle charging stations,an online modified RBF-NN short-term load forecasting model based on fuzzy control is proposed. The forecasting method uses fuzzy control theory to modify the results of the RBF-NN short-term load forecasting model online. Compared with single RBF-NN short-term load forecasting model,the accuracy of the forecasting model has been further improved,which proves the superiority of the forecasting model.
A large number of electric vehicle charging loads will impact the local power grid. In order to improve the load forecasting accuracy of electric vehicle charging stations,an online modified RBF-NN short-term load forecasting model based on fuzzy control is proposed. The forecasting method uses fuzzy control theory to modify the results of the RBF-NN short-term load forecasting model online. Compared with single RBF-NN short-term load forecasting model,the accuracy of the forecasting model has been further improved,which proves the superiority of the forecasting model.
引文
[1]陈晓露.交互式智能电网的负荷预测与定价机制[D].上海:上海交通大学,2013.CHEN Xiaolu. Interactive smart grid load forecasting and prcing mechanism[D]. Shanghai:Shanghai Jiaotong University,2013.
[2]彭显刚,胡松峰,吕大勇.基于RBF神经网络的短期负荷预测方法综述[J].电力系统保护与控制,2011,39(17):144-148.PENG Xiangang,HU Songfeng,LYU Dayong. Review on grid short-term load forecasting methods based on RBF neural network[J]. Power System Protection and Control, 2011,39(17):144-148.
[3]武小梅,白银明,文福拴.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制,2011,39(15):80-83.WU Xiaomei, Bai Yinming, WEN Fushuan. Short-term wind power forecast based on the Radial Basis Function neural network[J]. Power System Protection and Control, 2011,39(15):80-83.
[4]陆健.最小二乘法及其应用[J].中国西部科技,2007(12):115-117.LU Jian. The least square method and its application[J]. Science and Technology of West China,2007(12):115-117.
[5]刘向杰,周孝信,柴天佑.模糊控制研究的现状与新发展[J].信息与控制,1999,28(4):283-292.LIU Xiangjie,ZHOU Xiaoxin,CHAI Tianyou. Status and development of fuzzy control[J]. Information and Control,1999,28(4):283-292.
[6]刘曙光,王志宏,费佩燕,等.模糊控制的发展与展望[J].机电工程,2000,17(1):9-12.LIU Shuguang,WANG Zhihong,FEI Peiyan,et al. Advance and prospect of the fuzzy control[J]. Mechanical&Electrical Engineering,2000,17(1):9-12.
[7]张恩勤,施颂椒,翁正新.模糊控制与PID控制方法的比较[J].上海交通大学学报,1999,33(4):501-503.ZHANG Enqin,SHI Songjiao,WENG Zhengxin. Comparative study of fuzzy control and PID control[J]. Journal of Shanghai Jiaotong University,1999,33(4):501-503.
[8]赵宇红.基于模糊神经网络的电力系统短期负荷预测研究[D].衡阳:南华大学,2004.ZHAO Yuhong. Research on short-term load forecasting of power system based on fuzzy neural network[D]. Hengyang:Nanhua University,2004.
[9]张德玲,陈根永,郭飞.模糊规则控制的RBF短期负荷预测[J].微计算机信息,2007,23(34):253-254.ZHANG Deling,CHEN Genyong,GUO Fei. RBF short-term load forecasting based on fuzzy rule control[J]. Control&Automation,2007,23(34):253-254.
[10]姜勇.电力系统短期负荷预测的模糊神经网络方法[J].继电器,2002,30(7):1-3.JIANG Yong. Fuzzy neural network for short-term load forecasting[J].Relay,2002,30(7):11-13.
[2]彭显刚,胡松峰,吕大勇.基于RBF神经网络的短期负荷预测方法综述[J].电力系统保护与控制,2011,39(17):144-148.PENG Xiangang,HU Songfeng,LYU Dayong. Review on grid short-term load forecasting methods based on RBF neural network[J]. Power System Protection and Control, 2011,39(17):144-148.
[3]武小梅,白银明,文福拴.基于RBF神经元网络的风电功率短期预测[J].电力系统保护与控制,2011,39(15):80-83.WU Xiaomei, Bai Yinming, WEN Fushuan. Short-term wind power forecast based on the Radial Basis Function neural network[J]. Power System Protection and Control, 2011,39(15):80-83.
[4]陆健.最小二乘法及其应用[J].中国西部科技,2007(12):115-117.LU Jian. The least square method and its application[J]. Science and Technology of West China,2007(12):115-117.
[5]刘向杰,周孝信,柴天佑.模糊控制研究的现状与新发展[J].信息与控制,1999,28(4):283-292.LIU Xiangjie,ZHOU Xiaoxin,CHAI Tianyou. Status and development of fuzzy control[J]. Information and Control,1999,28(4):283-292.
[6]刘曙光,王志宏,费佩燕,等.模糊控制的发展与展望[J].机电工程,2000,17(1):9-12.LIU Shuguang,WANG Zhihong,FEI Peiyan,et al. Advance and prospect of the fuzzy control[J]. Mechanical&Electrical Engineering,2000,17(1):9-12.
[7]张恩勤,施颂椒,翁正新.模糊控制与PID控制方法的比较[J].上海交通大学学报,1999,33(4):501-503.ZHANG Enqin,SHI Songjiao,WENG Zhengxin. Comparative study of fuzzy control and PID control[J]. Journal of Shanghai Jiaotong University,1999,33(4):501-503.
[8]赵宇红.基于模糊神经网络的电力系统短期负荷预测研究[D].衡阳:南华大学,2004.ZHAO Yuhong. Research on short-term load forecasting of power system based on fuzzy neural network[D]. Hengyang:Nanhua University,2004.
[9]张德玲,陈根永,郭飞.模糊规则控制的RBF短期负荷预测[J].微计算机信息,2007,23(34):253-254.ZHANG Deling,CHEN Genyong,GUO Fei. RBF short-term load forecasting based on fuzzy rule control[J]. Control&Automation,2007,23(34):253-254.
[10]姜勇.电力系统短期负荷预测的模糊神经网络方法[J].继电器,2002,30(7):1-3.JIANG Yong. Fuzzy neural network for short-term load forecasting[J].Relay,2002,30(7):11-13.