Abstract
The electric power market is increasingly relying on competitive mechanisms taking the form of day-ahead auctions, in which buyers and sellers submit their bids in terms of prices and quantities for each hour of the next day. Methods for electricity price forecasting suitable for these contexts are crucial to the success of any bidding strategy. Such methods have thus become very important in practice, due to the economic relevance of electric power auctions.
In this work we propose a novel forecasting method based on Genetic Programming. Key feature of our proposal is the handling of outliers, i.e., regions of the input space rarely seen during the learning. Since a predictor generated with Genetic Programming can hardly provide acceptable performance in these regions, we use a classifier that attempts to determine whether the system is shifting toward a difficult-to-learn region. In those cases, we replace the prediction made by Genetic Programming by a constant value determined during learning and tailored to the specific subregion expected.
We evaluate the performance of our proposal against a challenging baseline representative of the state-of-the-art. The baseline analyzes a real-world dataset by means of a number of different methods, each calibrated separately for each hour of the day and recalibrated every day on a progressively growing learning set. Our proposal exhibits smaller prediction error, even though we construct one single model, valid for each hour of the day and used unmodified across the entire testing set. We believe that our results are highly promising and may open a broad range of novel solutions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amjady, N., Keynia, F.: Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method. International Journal of Electrical Power and Energy Systems 30(9), 533–546 (2008)
Areekul, P., Senjyu, T., Toyama, H., Yona, A.: A hybrid ARIMA and neural network model for Short-Term price forecasting in deregulated market. IEEE Transactions on Power Systems 25(1), 524–530 (2010)
Catalao, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: Hybrid Wavelet-PSO-ANFIS approach for Short-Term electricity prices forecasting. IEEE Transactions on Power Systems (99), 1–8 (2010)
Cuaresma, J.C., Hlouskova, J., Kossmeier, S., Obersteiner, M.: Forecasting electricity spot-prices using linear univariate time-series models. Applied Energy 77(1), 87–106 (2004)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning (1989)
Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proc. 17th Intern. Conf. Machine Learning, pp. 359–366 (2000)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explorations 11(1) (2009)
Koopman, S.J., Ooms, M.: Forecasting daily time series using periodic unobserved components time series models. Computational Statistics & Data Analysis 51(2), 885–903 (2006)
Mendes, E.F., Oxley, L., Reale, M.: Some new approaches to forecasting the price of electricity: a study of californian market, http://ir.canterbury.ac.nz/handle/10092/2069 , RePEc Working Paper Series: No. 05/2008
Mount, T.D., Ning, Y., Cai, X.: Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters. Energy Economics 28(1), 62–80 (2006)
Pedregal, D.J., Trapero, J.R.: Electricity prices forecasting by automatic dynamic harmonic regression models. Energy Conversion and Management 48(5), 1710–1719 (2007)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)
Sheblé, G.B.: Computational auction mechanisms for restructured power industry operation. Springer, Netherlands (1999)
Weron, R.: Misiorek: Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models. International Journal of Forecasting, 744–763 (2008)
Wu, L., Shahidehpour, M.: A hybrid model for Day-Ahead price forecasting. IEEE Transactions on Power Systems 25(3), 1519–1530 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bartoli, A., Davanzo, G., De Lorenzo, A., Medvet, E. (2011). GP-Based Electricity Price Forecasting. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds) Genetic Programming. EuroGP 2011. Lecture Notes in Computer Science, vol 6621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20407-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-20407-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20406-7
Online ISBN: 978-3-642-20407-4
eBook Packages: Computer ScienceComputer Science (R0)