Point and prediction interval estimation for electricity markets with machine learning techniques and wavelet transforms

Neurocomputing - Tập 118 - Trang 301-310 - 2013
Nitin Anand Shrivastava1, Bijaya Ketan Panigrahi1
1Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India

Tài liệu tham khảo

Zareipour, 2006, Application of public-domain market information to forecast Ontario's wholesale electricity prices, IEEE Trans. Power Syst., 21, 1707, 10.1109/TPWRS.2006.883688 Zhou, 2006, Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach, IEE Proc. Gen. Transm. Distrib., 153, 187, 10.1049/ip-gtd:20045131 Garcia-Martos, 2007, Mixed models for short-run forecasting of electricity prices, IEEE Trans. Power Syst., 22, 544, 10.1109/TPWRS.2007.894857 Garcia, 2005, A GARCH forecasting model to predict day-ahead electricity prices, IEEE Trans. Power Syst., 20, 867, 10.1109/TPWRS.2005.846044 Amjady, 2006, Energy price forecasting—problems and proposals for such predictions, IEEE Power Energy, 4, 20, 10.1109/MPAE.2006.1597990 Hong, 2002, Locational marginal price forecasting in deregulated electricity markets using artificial intelligence, IEE Proc. Gen. Transm. Distrib., 149, 621, 10.1049/ip-gtd:20020371 Zhang, 2003, Energy clearing price prediction and confidence interval estimation with cascaded neural networks, IEEE Trans. Power Syst., 18, 99, 10.1109/TPWRS.2002.807062 Guo, 2004, Improving market clearing price prediction by using a committee machine of neural networks, IEEE Trans. Power Syst., 19, 1867, 10.1109/TPWRS.2004.837759 Gonzalez, 2005, Modeling and forecasting electricity prices with input/output hidden Markov models, IEEE Trans. Power Syst., 20, 13 Amjady, 2006, Day-ahead price forecasting of electricity markets by a new fuzzy neural network, IEEE Trans. Power Syst., 21, 887, 10.1109/TPWRS.2006.873409 Rodriguez, 2004, Energy price forecasting in the Ontario competitive power system market, IEEE Trans. Power Syst., 19, 366, 10.1109/TPWRS.2003.821470 Fan, 2007, Next-day electricity-price forecasting using a hybrid network, IET Gen. Transm. Distrib., 1, 176, 10.1049/iet-gtd:20060006 Huang, 2006, Extreme learning machine, Neurocomputing, 70, 489, 10.1016/j.neucom.2005.12.126 Zhu, 2005, Evolutionary extreme learning machine, Pattern Recognition, 38, 1759, 10.1016/j.patcog.2005.03.028 Huang, 2003, Learning capability and storage capacity of two-hidden-layer feedforward networks, IEEE Trans. Neural Networks, 14, 274, 10.1109/TNN.2003.809401 Huang, 1998, Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions, IEEE Trans. Neural Networks, 9, 224, 10.1109/72.655045 Huang, 2006, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks, 17, 879, 10.1109/TNN.2006.875977 Bishop, M. Christopher, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, U.K., USA, 1996. Tom Heskes, Practical Confidence and Prediction Intervals, Advances in Neural Information Processing Systems 9, MIT Press, 1997, pp. 176–182. Hwang, 1997, Prediction intervals for artificial neural networks, J. Am. Stat. Assoc., 92, 748, 10.1080/01621459.1997.10474027 Efron, 1993 Davison, 1997 Zio, 2006, A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient process, IEEE Trans. Nucl. Sci., 53, 1460, 10.1109/TNS.2006.871662 Mazloumi, 2011, Prediction intervals to account for uncertainties in neural network predictions, Eng. Appl. Artif. Intell., 24, 534, 10.1016/j.engappai.2010.11.004 Khosravi, 2010, A prediction interval-based approach to determine optimal structures of neural network metamodels, Expert Syst. Appl., 37, 2377, 10.1016/j.eswa.2009.07.059 Abbas Khosravi, Saeid Nahavandi, Doug Creighton, Constructing prediction intervals for neural network metamodels of complex systems, in: International Joint Conference on Neural Networks (IJCNN), 2009, pp. 1576–1582. Benoit, 2009, Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting, Expert Syst. Appl., 36, 10475, 10.1016/j.eswa.2009.01.031 Shrestha, 2006, Machine learning approaches for estimation of prediction interval for the model output, Neural Networks, 19, 225, 10.1016/j.neunet.2006.01.012 Zhao, 2008, A statistical approach for interval forecasting of the electricity price, IEEE Trans. Power Syst., 23, 267, 10.1109/TPWRS.2008.919309 Khosravi, 2010, Construction of optimal prediction intervals for load forecasting problem, IEEE Trans. Power Syst., 25, 1496, 10.1109/TPWRS.2010.2042309 Richard Dybowski and Stephen J. Roberts, Confidence intervals and prediction intervals for feedforward neural networks, Clinical Applications of Artificial Neural Networks, Cambridge University Press, 2001. Rodriguez, 2004, Energy price forecasting in the Ontario competitive power system market, IEEE Trans. Power Syst., 19, 366, 10.1109/TPWRS.2003.821470 Hornik, 1989, Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359, 10.1016/0893-6080(89)90020-8 Serre, 2002 Rao, 1971 Lokenath Debnath, Wavelet Transforms and their Applications, PINSA-A, 64, A, No. 6, November 1998, pp. 685–713. Yoo, 2002, Signal-dependent wavelet transform and application to lossless image compression, Electron. Lett., 38, 170 Boix, 2010, Wavelet transform application to the compression of images, Math. Comput. Model., 52, 1265, 10.1016/j.mcm.2010.02.019 Qiao, 2009, Complex wavelet based texture classification, Neurocomputing, 72, 3957, 10.1016/j.neucom.2009.06.003 Manimala, 2012, Optimization techniques for improving power quality data mining using wavelet packet based support vector machine, Neurocomputing, 77, 36, 10.1016/j.neucom.2011.08.010 Maheswaran, 2012, Comparative study of different wavelets for hydrologic forecasting, Comput. Geosci., 46, 284, 10.1016/j.cageo.2011.12.015 Simons, 2006, Automatic detection and rapid determination of earthquake magnitude by wavelet multiscale analysis of the primary arrival, Earth Planet. Sci. Lett., 250, 214, 10.1016/j.epsl.2006.07.039 Conejo, 2005, Day-ahead electricity price forecasting using the wavelet transform and arima models, IEEE Trans. Power Syst., 20, 1035, 10.1109/TPWRS.2005.846054 Nievergelt, 1999 Rocha Reis, 2005, Feature extraction via multi-resolution analysis for short term load forecasting, IEEE Trans. Power Syst., 20, 189, 10.1109/TPWRS.2004.840380 Box, George Edward Pelham, Jenkins, Gwilym, Time Series Analysis, Forecasting and Control, Holden-Day, Incorporated, 1990. Cao, 1997, Saving computer time in constructing consistent bootstrap prediction intervals for autoregressive processes, Commun. Stat. Simul. Comput., 26, 961, 10.1080/03610919708813420 Efron, 1979, Bootstrap methods, Ann. Stat., 7, 1, 10.1214/aos/1176344552 Stine, 1987, Estimating properties of autoregressive forecasts, J. Am. Stat. Assoc., 82, 1072, 10.1080/01621459.1987.10478542 Thombs, 1990, Bootstrap prediction intervals for autoregression, J. Am. Stat. Assoc., 85, 486, 10.1080/01621459.1990.10476225 Weron, 2006 Khosravi, 2011, Comprehensive review of neural network-based prediction intervals and new advances, IEEE Trans. Neural Networks, 22, 1341, 10.1109/TNN.2011.2162110 Website of Ontario Electricity Market [Online]. Available from 〈http://www.ieso.ca〉. PJM Website [Online]. Available from 〈http://www.pjm.com〉. Kohavi, 1997, Wrappers for feature subset selection, Artif. Intell., 97, 273, 10.1016/S0004-3702(97)00043-X