Prediction intervals for electricity load forecasting using neural networks

Most of the research in time series is concerned with point forecasting. In this paper we focus on interval forecasting and its application for electricity load prediction. We extend the LUBE method, a neural network-based method for computing prediction intervals. The extended method, called LUBEX, includes an advanced feature selector and an ensemble of neural networks. Its performance is evaluated using Australian electricity load data for one year. The results showed that LUBEX is able to generate high quality prediction intervals, using a very small number of previous lag variables and having acceptable training time requirements. The use of ensemble is shown to be critical for the accuracy of the results.

[1]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[2]  Abbas Khosravi,et al.  Construction of neural network-based prediction intervals using particle swarm optimization , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[3]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[4]  Irena Koprinska,et al.  Yearly and seasonal models for electricity load forecasting , 2011, The 2011 International Joint Conference on Neural Networks.

[5]  Ying Chen,et al.  Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks , 2010, IEEE Transactions on Power Systems.

[6]  Quan Chen,et al.  A neural network based very short term load forecaster for the interim ISO New England electricity market system , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).

[7]  W. Charytoniuk,et al.  Very short-term load forecasting using artificial neural networks , 2000 .

[8]  J. W. Taylor,et al.  Short-term electricity demand forecasting using double seasonal exponential smoothing , 2003, J. Oper. Res. Soc..

[9]  Tom Heskes,et al.  Practical Confidence and Prediction Intervals , 1996, NIPS.

[10]  James W. Taylor.  An evaluation of methods for very short-term load forecasting using minute-by-minute British data , 2008 .

[11]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[12]  James W. Taylor,et al.  Triple seasonal methods for short-term electricity demand forecasting , 2010, Eur. J. Oper. Res..

[13]  Irena Koprinska,et al.  Electricity load forecasting based on autocorrelation analysis , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[14]  Amir F. Atiya,et al.  Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals , 2011, IEEE Transactions on Neural Networks.

[15]  Abbas Khosravi,et al.  Particle swarm optimization for construction of neural network-based prediction intervals , 2014, Neurocomputing.

[16]  Irena Koprinska,et al.  Electricity Load Forecasting: A Weekday-Based Approach , 2012, ICANN.

[17]  J. T. Hwang,et al.  Prediction Intervals for Artificial Neural Networks , 1997 .

[18]  Chris Chatfield,et al.  Time‐series forecasting , 2000 .

[19]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[20]  David J. C. MacKay,et al.  The Evidence Framework Applied to Classification Networks , 1992, Neural Computation.

[21]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.