基于小波分解和布谷鸟搜索算法的小波神经网络风速预测（英文）
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摘要
风速预测是风电场运行和风电并网过程中的关键技术之一。由于风速序列呈现出明显的间歇性和波动性,使用单一模型进行时预测难以取得满意的结果。本文发展了三种混合多步预测模型,并将他们与已有的风速预测模型相比较。这三个模型结合了小波分解、布谷鸟搜索算法和小波神经网络,分别记为CS-WD-ANN,CS-WNN和CS-WD-WNN。研究采用中国山东省两个风电场的实测数据进行模拟试验和模型比较,结果显示CS-WD-WNN表现最佳,具有最低的统计误差。
Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and compared — with each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition(WD), the Cuckoo search(CS) optimization algorithm, and a wavelet neural network(WNN). They are referred to as CS-WD-ANN(artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.
Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and compared — with each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition(WD), the Cuckoo search(CS) optimization algorithm, and a wavelet neural network(WNN). They are referred to as CS-WD-ANN(artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.
引文
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Valian,E.,and E.Valian.2013.“A Cuckoo Search Algorithm by Levy Flights for Solving Reliability Redundancy Allocation Problems.”Engineering Optimization 45:1273-1286.doi:10.1080/0305215X.2012.729055.
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Yang,X.S.,and S.Deb.2009.“Cuckoo Search via L′Evy flights.”Proceedings of World Congress on Nature&Biologically Inspired Computing(NaBIC),210-214.India,December.IEEE Publications,USA.doi:10.1109/nabic.2009.5393690.
Zhang,L.,K.Wu,Y.Zhong,and P.Li.2008.“A New Sub-Pixel Mapping Algorithm Based on A BP Neural Network with an Observation Model.”Neurocomputing 71:2046-2054.doi:10.1016/j.neucom.2007.08.033.
Zhang,Q.G.,and A.Benveniste.1992.“Wavelet Networks.”IEEE Transactions on Neural Networks 3:889-898.doi:10.1109/72.165591.
Zhang,W.Y.,Z.Y.Su,H.L.Zhang,Y.R.Zhao,and Z.Y.Zhao.2014.“Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm.”Abstract and Applied Analysis 2014:1-14.doi:10.1155/2014/693205.
Zhang,Y.G.,C.H.Zhang,Y.Zhao,and S.Gao.2018.“Wind Speed Prediction with RBF Neural Network Based on PCAand ICA.”Journal Of Electrical Engineering-Elektrotechnicky Casopis 69(2):148-155.doi:10.2478/jee-2018-0018.
Zhao,J.,Z.H.Guo,Z.Y.Su,Z.Y.Zhao,X.Xiao,and F.Liu.2016.“An Improved Multi-Step Forecasting Model Based on WRF Ensembles and Creative Fuzzy Systems for Wind Speed.”Applied Energy 162:808-826.doi:10.1016/j.apenergy.2015.10.145.
Zheng,D.H.,Y.K.Semero,J.H.Zhang,and D.Wei.2018.“Short-Term Wind Power Prediction in Microgrids Using a Hybrid Approach Integrating Genetic Algorithm,Particle Swarm Optimization,and Adaptive Neuro-Fuzzy Inference Systems.”Ieej Transactions On Electrical And Electronic Engineering 13(11):1561-1567.doi:10.1002/tee.22720.
Babu,R.N.,and P.Arulmozhivarman.2013.“Improving Forecast Accuracy of Wind Speed Using Wavelet Transform and Neural Networks.”Journal of Electrical Engineering&Technology 8:559-564.doi:10.5370/JEET.2013.8.3.559.
Chandra,D.R.,M.S.Kumari,M.Sydulu,F.Grimaccia,and M.Mussetta.2014.“Adaptive Wavelet Neural Network Based Wind Speed Forecasting Studies.”Journal of Electrical Engineering&Technology 9:1812-1821.doi:10.5370/JEET.2014.9.6.1812.
Chitsaz,H.,N.Amjady,and H.Zareipour.2015.“Wind Power Forecast Using Wavelet Neural Network Trained by Improved Clonal Selection Algorithm.”Energy Convers Manage 89:588-598.doi:10.1016/j.enconman.2014.10.001.
Doucoure,B.,K.Agbossou,and A.Cardenas.2016.“Time Series Prediction Using Artificial Wavelet Neural Network and Multi-Resolution Analysis:Application to Wind Speed Data.”Renewable Energy 92:202-211.doi:10.1016/j.renene.2016.02.003.
Falamarzi,Y.,N.Palizdan,Y.F.Huang,and T.S.Lee.2014.“Estimating Evapotranspiration from Temperature and Wind Speed Data Using Artificial and Wavelet Neural Networks(Wnns).”Agricultural Water Management 140:26-36.doi:10.1016/j.agwat.2014.03.014.
GWEC(Global Wind Energy Council).2018.Global Wind Statistics 2017,4.Global Wind Energy Council Rep.
Landberg,L.1999.“Short-Term Prediction of the Power Production from Wind Farms.”Journal of Wind Engineering and Industrial Aerodynamics 80:207-220.doi:10.1016/S0167-6105(98)00192-5.
Lange,M.,and U.Focken.2009.Physical Approach to ShortTerm Wind Power Prediction.New York:Springer-Verlag.
Liu,D.,G.X.Li,Q.Fu,M.Li,C.L.Liu,M.A.Faiz,M.I.Khan,T.X.Li,and S.Cui.2018.“Application of Particle Swarm Optimization and Extreme Learning Machine Forecasting Models for Regional Groundwater Depth Using Nonlinear Prediction Models as Preprocessor.”Journal Of Hydrologic Engineering 23(12):04018052:1-11.doi:10.1061/(ASCE)HE.1943-5584.0001711.
Liu,H.,H.Q.Tian,D.F.Pan,and Y.F.Li.2013.“Forecasting Models for Wind Speed Using Wavelet,Wavelet Packet,Time Series and Artificial Neural Networks.”Applied Energy107:191-208.doi:10.1016/j.apenergy.2013.02.002.
Ma,L.,S.Y.Luan,C.W.Jiang,H.L.Liu,and Y.Zhang.2009.“A Review on the Forecasting of Wind Speed and Generated Power.”Renewable&Sustainable Energy Reviews 13:915-920.doi:10.1016/j.rser.2008.02.002.
Madhiarasan,M.,and S.N.Deepa.2017.“Comparative Analysis on Hidden Neurons Estimation in Multi Layer Perceptron Neural Networks for Wind Speed Forecasting.”Artificial Intelligence Review 48(4):449-471.doi:10.1007/s10462-016-9506-6.
Negnevitsky,M.,and C.W.Potter.2006.“Innovative Short-Term Wind Generation Prediction Techniques.”IEEEPower Engineering Society General Meeting.Montreal,June 18-22.doi:10.1109/PES.2006.1709026
Philibert,C.,H.Holttinen,P.Frankl,K.Sadamori,C.Tam,H.Bahar,D.Moeller,et al.2013.Technology Roadmap.Wind Energy.Paris:International Energy Agency.
Rumelhart,D.E.,G.E.Hintont,and R.J.Williams.1986.“Learning Representations by Back Propagating Errors.”Nature 323:533-536.doi:10.1038/323533a0.
Salcedo-Sanz,S.,A.M.Perez-Bellido,E.G.Ortiz-Garcia,A.Portilla-Figueras,L.Prieto,and F.Correoso.2009.“Accurate Short-Term Wind Speed Prediction by Exploiting Diversity in Input Data Using Banks of Artificial Neural Networks.”Neurocomputing 72:1336-1341.doi:10.1016/j.neucom.2008.09.010.
Shao,H.J.,X.Deng,and Y.T.Jiang.2018.“A Novel Deep Learning Approach for Short-Term Wind Power Forecasting Based on Infinite Feature Selection and Recurrent Neural Network.”Journal Of Renewable And Sustainable Energy 10(4):1-12.doi:10.1063/1.5024297.
Shi,Z.C.,H.Liang,and V.Dinavahi.2018.“Direct Interval Forecast of Uncertain Wind Power Based on Recurrent Neural Networks.”IEEE Transactions On Sustainable Energy9(3):1177-1187.doi:10.1109/TSTE.2017.2774195.
Siwek,K.,and S.Osowski.2012.“Improving the Accuracy of Prediction of PM10 Pollution by the Wavelet Transformation and an Ensemble of Neural Predictors.”Engineering Applications of Artificial Intelligence 25:1246-1258.doi:10.1016/j.engappai.2011.10.013.
Valian,E.,and E.Valian.2013.“A Cuckoo Search Algorithm by Levy Flights for Solving Reliability Redundancy Allocation Problems.”Engineering Optimization 45:1273-1286.doi:10.1080/0305215X.2012.729055.
Wu,J.,J.Z.Wang,and D.Z.Chi.2013.“Wind Energy Potential Assessment for the Site of Inner Mongolia in China.”Renewable&Sustainable Energy Reviews 21:215-228.doi:10.1016/j.rser.2012.12.060.
Yang,X.S.,and S.Deb.2009.“Cuckoo Search via L′Evy flights.”Proceedings of World Congress on Nature&Biologically Inspired Computing(NaBIC),210-214.India,December.IEEE Publications,USA.doi:10.1109/nabic.2009.5393690.
Zhang,L.,K.Wu,Y.Zhong,and P.Li.2008.“A New Sub-Pixel Mapping Algorithm Based on A BP Neural Network with an Observation Model.”Neurocomputing 71:2046-2054.doi:10.1016/j.neucom.2007.08.033.
Zhang,Q.G.,and A.Benveniste.1992.“Wavelet Networks.”IEEE Transactions on Neural Networks 3:889-898.doi:10.1109/72.165591.
Zhang,W.Y.,Z.Y.Su,H.L.Zhang,Y.R.Zhao,and Z.Y.Zhao.2014.“Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm.”Abstract and Applied Analysis 2014:1-14.doi:10.1155/2014/693205.
Zhang,Y.G.,C.H.Zhang,Y.Zhao,and S.Gao.2018.“Wind Speed Prediction with RBF Neural Network Based on PCAand ICA.”Journal Of Electrical Engineering-Elektrotechnicky Casopis 69(2):148-155.doi:10.2478/jee-2018-0018.
Zhao,J.,Z.H.Guo,Z.Y.Su,Z.Y.Zhao,X.Xiao,and F.Liu.2016.“An Improved Multi-Step Forecasting Model Based on WRF Ensembles and Creative Fuzzy Systems for Wind Speed.”Applied Energy 162:808-826.doi:10.1016/j.apenergy.2015.10.145.
Zheng,D.H.,Y.K.Semero,J.H.Zhang,and D.Wei.2018.“Short-Term Wind Power Prediction in Microgrids Using a Hybrid Approach Integrating Genetic Algorithm,Particle Swarm Optimization,and Adaptive Neuro-Fuzzy Inference Systems.”Ieej Transactions On Electrical And Electronic Engineering 13(11):1561-1567.doi:10.1002/tee.22720.