基于改进权值优化模型的光伏功率区间预测
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摘要
区间预测方法可以反映光伏发电功率可能的变化范围,提供比点预测方法更丰富的预测信息。文章提出了一种基于径向基函数(radial basis function,RBF)神经网络的区间预测模型以直接输出光伏功率预测区间。为优化模型输出区间的性能和避免惩罚系数选择问题,构建了一种考虑区间预测偏差信息的改进预测区间优化模型,并采用粒子群算法(particle swarm optimization,PSO)求解,获得最优RBF神经网络输出权值以提高预测区间的可信度和准确性。通过对比传统区间优化模型和所提改进区间优化模型的预测结果,发现改进区间优化模型能够获得宽度更窄和预测偏差更小的光伏功率预测区间,可为调度决策提供更准确的辅助信息。
Interval prediction method can reflect the possible range of photovoltaic power,and provide more abundant forecasting information than point prediction method. An interval prediction model based on Radial Basis Function(RBF) neural network is proposed to directly output the prediction interval of photovoltaic power. In order to optimize the performance of the output interval of the model and avoid the problem of choosing penalty coefficient,an improved prediction interval optimization model considering the bias information of the interval prediction is constructed and solved by particle swarm optimization(PSO)algorithm to obtain the optimal RBF neural network output weights,and improve the reliability and accuracy of the prediction interval. By comparing the prediction results of the traditional interval optimization model and the improved interval optimization model,it is found that the improved interval optimization model can obtain more accurate prediction interval of photovoltaic power,and can provide more accurate auxiliary information for dispatching decision-making.
Interval prediction method can reflect the possible range of photovoltaic power,and provide more abundant forecasting information than point prediction method. An interval prediction model based on Radial Basis Function(RBF) neural network is proposed to directly output the prediction interval of photovoltaic power. In order to optimize the performance of the output interval of the model and avoid the problem of choosing penalty coefficient,an improved prediction interval optimization model considering the bias information of the interval prediction is constructed and solved by particle swarm optimization(PSO)algorithm to obtain the optimal RBF neural network output weights,and improve the reliability and accuracy of the prediction interval. By comparing the prediction results of the traditional interval optimization model and the improved interval optimization model,it is found that the improved interval optimization model can obtain more accurate prediction interval of photovoltaic power,and can provide more accurate auxiliary information for dispatching decision-making.
引文
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[17]赵维嘉,张宁,康重庆,等.光伏发电出力的条件预测误差概率分布估计方法[J].电力系统自动化,2015,39(16):8-15.ZHAO Weijia,ZHANG Ning,KANG Chongqing,et al.A method of probabilistic distribution estimation of conditional forecast error for PV generation[J].Automation of Electric Power Systems,2015,39(16):8-15.
[18]程泽,刘冲,刘力.基于相似时刻的光伏出力概率分布估计方法[J].电网技术,2017,41(2):448-455.CHENG Ze,LIU Chong,LIU Li.A method of probabilistic distribution estimation of PV generation based on similar time of day[J].Power System Technology,2017,41(2):448-455.
[19]KHOSRAVI A,NAHAVANDI S,CREIGHTON D,et al.Lower upper bound estimation method for construction of neural net-workbased prediction intervals[J].IEEE Transactions on Neural Networks,2011,22(3):337-346.
[20]HAO Q,SRINIVASAN D,KHOSRAVI A.Short-term load and wind power forecasting using neural network-based prediction intervals[J].IEEE Transactions on Neural Networks,2014,25(2):303-315.
[21]史峰,王小川,郁磊,等.MATLAB神经网络30个案例分析[M].北京:北京航空航天大学出版社,2010:66-67.
[22]张振宇,葛少云,刘自发.粒子群优化算法及其在机组优化组合中应用[J].电力自动化设备,2006,26(5):28-31.ZHANG Zhenyu,GE Shaoyun,LIU Zifa.Particle swarm optimization algorithm and its application in unit commit-ment[J].Electric Power Automation Equipment,2006,26(5):28-31.
[23]杨锡运,关文渊,刘玉奇,等.基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J].中国电机工程学报,2015,35(S1):146-153.YANG Xiyun,GUAN Wenyuan,LIU Yuqi,et al.Prediction intervals forecasts of wind power based on PSO-KELM[J].Proceedings of the CSEE,2015,35(S1):146-153.
[2]我国光伏发电累计装机规模超过17000万千瓦[EB/OL].(2019-01-18)[2019-04-01].http://www.cec.org.cn/hangyeguangjiao/lvsenengyuan/2019-01-18/188304.html.
[3]丁明,王伟胜,王秀丽,等.大规模光伏发电对电力系统影响综述[J].中国电机工程学报,2014,34(1):1-14.DING Ming,WANG Weisheng,WANG Xiuli,et al.A review on the effect of large-scale PV generation on power systems[J].Proceedings of the CSEE,2014,34(1):1-14.
[4]赖昌伟,黎静华,陈博,等.光伏发电出力预测技术研究综述[J].电工技术学报,2019,34(6):1201-1217.LAI Changwei,LI Jinghua,CHEN Bo,et al.Review of photovoltaic power output prediction technology[J].Transactions of China Electrotechnical Society,2019,34(6):1201-1217.
[5]龚莺飞,鲁宗相,乔颖,等.光伏功率预测技术[J].电力系统自动化,2016,40(4):140-151.GONG Yingfei,LU Zongxiang,QIAO Ying,et al.An overview of photovoltaic energy system output forecasting technology[J].Automation of Electric Power Systems,2016,40(4):140-151.
[6]高阳,张碧玲,毛京丽,等.基于机器学习的自适应光伏超短期出力预测模型[J].电网技术,2015,39(2):307-311.GAO Yang,ZHANG Biling,MAO Jingli,et al.Machine learningbased adaptive very-short-term forecast model for photovoltaic power[J].Power System Technology,2015,39(2):307-311.
[7]叶林,陈政,赵永宁,等.基于遗传算法-模糊径向基神经网络的光伏发电功率预测模型[J].电力系统自动化,2015,39(16):16-22.YE Lin,CHEN Zheng,ZHAO Yongning,et al.Photovoltaic power forecasting model based on genetic algorithm and fuzzy radial basis functiom neural network[J].Automation of Electric Power Systems,2015,39(16):16-22.
[8]陈通,孙国强,卫志农,等.基于相似日和CAPSO-SNN的光伏发电功率预测[J].电力自动化设备,2017,37(3):66-71.CHEN Tong,SUN Guoqiang,WEI Zhinong,et al.Photovoltaic power generation forecasting based on similar day and CAPSO-SNN[J].Electric Power Automation Equipment,2017,37(3):66-71.
[9]张程熠,唐雅洁,李永杰,等.适用于小样本的神经网络光伏预测方法[J].电力自动化设备,2017,37(1):101-106,111.ZHANG Chengyi,TANG Yajie,LI Yongjie,et al.Photovoltaic power forecast based on neural network with a small number of samples[J].Electric Power Automation Equipment,2017,37(1):101-106,111.
[10]王飞,米增强,甄钊,等.基于天气状态模式识别的光伏电站发电功率分类预测方法[J].中国电机工程学报,2013,33(34):75-82,14.WANG Fei,MI Zengqiang,ZHEN Zhao,et al.A classified forecasting approach of power generation for photovoltaic plants based on weather condition pattern recognition[J].Proceedings of the CSEE,2013,33(34):75-82,14.
[11]傅美平,马红伟,毛建容.基于相似日和最小二乘支持向量机的光伏发电短期预测[J].电力系统保护与控制,2012,40(16):65-69.FU Meiping,MA Hongwei,MAO Jianrong.Short-term photovoltaic power forecasting based on similar days and least square support vector machine[J].Power System Protection and Control,2012,40(16):65-69.
[12]王昕,黄柯,郑益慧,等.基于萤火虫算法-广义回归神经网络的光伏发电功率组合预测[J].电网技术,2017,41(2):455-461.WANG Xin,HUANG Ke,ZHENG Yihui,et al.Combined PVpower forecast based on firefly algorithm-generalized regression neural network[J].Power System Technology,2017,41(2):455-461.
[13]王守相,张娜.基于灰色神经网络组合模型的光伏短期出力预测[J].电力系统自动化,2012,36(19):37-41.WANG Shouxiang,ZHANG Na.Short-term out put power forecast of photovoltaic based on a grey and neural network hybrid model[J].Automation of Electric Power Systems,2012,36(19):37-41.
[14]丁明,刘志,毕锐,等.基于灰色系统校正-小波神经网络的光伏功率预测[J].电网技术,2015,39(9):2438-2443.DING Ming,LIU Zhi,BI Rui,et al.Photovoltaic output prediction based on grey system correction-wavelet neural network[J].Power System Technology,2015,39(9):2438-2443.
[15]兰飞,桑川川,梁浚杰,等.基于条件Copula函数的风电功率区间预测[J].中国电机工程学报,2016,36(S1):79-86.LAN Fei,SANG Chuanchuan,LIANG Junjie,et al.Interval prediction for wind power based on conditional copula function[J].Proceedings of the CSEE,2016,36(S1):79-86.
[16]FONSECA Junior J G D S,OOZEKI T,OHTAKE H,et al.On the use of maximum likelihood and input data similarity to obtain prediction intervals for forecasts of photovoltaic power generation[J].Journal of Electrical Engineering&Technology,2015,10(3):1342-1348.
[17]赵维嘉,张宁,康重庆,等.光伏发电出力的条件预测误差概率分布估计方法[J].电力系统自动化,2015,39(16):8-15.ZHAO Weijia,ZHANG Ning,KANG Chongqing,et al.A method of probabilistic distribution estimation of conditional forecast error for PV generation[J].Automation of Electric Power Systems,2015,39(16):8-15.
[18]程泽,刘冲,刘力.基于相似时刻的光伏出力概率分布估计方法[J].电网技术,2017,41(2):448-455.CHENG Ze,LIU Chong,LIU Li.A method of probabilistic distribution estimation of PV generation based on similar time of day[J].Power System Technology,2017,41(2):448-455.
[19]KHOSRAVI A,NAHAVANDI S,CREIGHTON D,et al.Lower upper bound estimation method for construction of neural net-workbased prediction intervals[J].IEEE Transactions on Neural Networks,2011,22(3):337-346.
[20]HAO Q,SRINIVASAN D,KHOSRAVI A.Short-term load and wind power forecasting using neural network-based prediction intervals[J].IEEE Transactions on Neural Networks,2014,25(2):303-315.
[21]史峰,王小川,郁磊,等.MATLAB神经网络30个案例分析[M].北京:北京航空航天大学出版社,2010:66-67.
[22]张振宇,葛少云,刘自发.粒子群优化算法及其在机组优化组合中应用[J].电力自动化设备,2006,26(5):28-31.ZHANG Zhenyu,GE Shaoyun,LIU Zifa.Particle swarm optimization algorithm and its application in unit commit-ment[J].Electric Power Automation Equipment,2006,26(5):28-31.
[23]杨锡运,关文渊,刘玉奇,等.基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J].中国电机工程学报,2015,35(S1):146-153.YANG Xiyun,GUAN Wenyuan,LIU Yuqi,et al.Prediction intervals forecasts of wind power based on PSO-KELM[J].Proceedings of the CSEE,2015,35(S1):146-153.