Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference
- 1
- 2
Abstract
This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.
- M. Shahidehpour, H. Yamin and Z. Li, “Market Operations in Electric Power Systems,” John Wiley & Sons, Chichester, 2002. doi:10.1002/047122412X
- A. K. Topalli, I. Erkmen and I. Topalli, “Intelligent Short-term Load Forecasting in Turkey,” Electrical Power and Energy Systems, Vol. 28, 2006, pp. 437-447. doi: 10.1016/j.ijepes.2006.02.004
- R. C. Garcia, et al., “GARCH Forecasting Model to Predict Day-ahead Electricity Prices,” IEEE Transactions on Power Systems, Vol. 20, No. 2, May 2005, pp. 867-874. doi:10.1109/TPWRS.2005.846044
- M. Stevenson, “Filtering and Forecasting Spot Electricity Prices in the Increasingly Deregulated Australian Electricity Market,” Quantitative Finance Research Centre, University of Technology, Sydney, 2001.
- N. Hubele, et al., “Identification of Seasonal Short-term Load Forecasting Models Using Statistical Decision Functions,” IEEE Transactions on Power Systems, Vol. 5, No. 1, 1990, pp. 40-5. doi:10.1109/59.49084
- M. El-Hawary, et al, “Short-Term Power System Load Forecasting Using the Iteratively Reweighted Least Squares Algorithm,” Electrical Power Systems Research, Vol. 19, 1990, pp. 11-22. doi:10.1016/0378-7796(90)900 03-L
- V. S. Kodogiannis and E. M. Anagnostakis, “A Study of Advanced Learning Algorithms for Short-term Load Forecasting,” Engineering Applications of Artificial Intelligence , Vol. 12, 1999, pp. 159-173. doi:10.1016/S0952- 1976(98)00064-5
- G.-C. Liao and T.-P. Tsao, “Application of Fuzzy Neural Networks and Artificial Intelligence for Short-term load Forecasting,” Electrical Power Systems Research, Vol. 70, 2004, pp. 237-244. doi:10.1016/j.epsr. 2003.12.012
- H. Yamin, M. Shahidehpour and Z. Li, “Adaptive short-term Price Forecasting using artificial Neural Networks in the Restructured Power Markets,” Electrical Power and Energy Systems, Vol. 26, 2004, pp. 571-581. doi:10.1016/j.ijepes.2004.04.005
- P. Mandal, T. Senjyu, N. Urasaki and T. Funabashi, “A Neural Network Based Several-Hour-Ahead Electric Load Forecasting using Similar Days Approach,” Electrical Power and Energy Systems, Vol. 28, 2006, pp. 367-373. doi:10.1016/j.ijepes.2005.12.007
- S. Rahman and R. Bhatnager, “An Expert System based Algorithm for Short Term Load Forecast,” IEEE Transactions on Power Systems, Vol. 3, No. 2, 1988, pp. 392- 399. doi:10.1109/59.192889
- Q. Lu, et al., “An Adaptive Nonlinear Predictor with Orthogonal Escalator Structure for Short-term Load Forecasting,” IEEE Transactions on Power Systems, Vol. 4, No. 1, 1989, pp. 158-164. doi:10.1109/59.32473