Abstract
We present a new approach for building weekday-based prediction models for electricity load forecasting. The key idea is to conduct a local feature selection using autocorrelation analysis for each day of the week and build a separate prediction model using linear regression and backpropagation neural networks. We used two years of 5-minute electricity load data for the state of New South Wales in Australia to evaluate performance. Our results showed that the weekday-based local prediction model, when used with linear regression, obtained a small and statistically significant increase in accuracy in comparison with the global (one for all days) prediction model. Both models, local and global, when used with linear regression were accurate and fast to train and are suitable for practical applications.
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
Taylor, J.W.: An evaluation of methods for very short-term load forecasting using minite-by-minute British data. International Journal of Forecasting 24, 645–658 (2008)
Taylor, J.W.: Short-term electricity demand forecasting using double seasonal exponencial smoothing. Journal of the Operational Research Society 54, 799–805 (2003)
Taylor, J.W.: Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research 204, 139–152 (2010)
Charytoniuk, W., Chen, M.-S.: Very short-term load forecasting using artificial neutal networks. IEEE Transactions on Power Systems 15, 263–268 (2000)
Shamsollahi, P., Cheung, K.W., Chen, Q., Germain, E.H.: A neural network based VSTLF for the interim ISO New England electricity market system. In: 22nd IEEE PICA Conf., pp. 217–222 (2001)
Sood, R., Koprinska, I., Agelidis, V.: Electricity load forecasting based on autocorrelation analysis. In: International Joint Conference on Neural Networks (IJCNN), Barcelona, pp. 1772–1779 (2010)
Koprinska, I., Rana, M., Agelidis, V.: Yearly and Seasonal Models for Electricity Load Forecasting. In: International Joint Conference on Neural Networks (IJCNN 2011), USA (2011)
Chen, B.J., Chang, M.-W., Lin, C.-J.: Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Transactions on Power Systems 19, 1821–1830 (2001)
Reis, A.J.R., Alvis, A.P., da Silva, P.A.: Feature Extraction via Multiresolution Analysis for Short-Term Load Forecasting. IEEE Transactions on Power Systems 20, 189–198 (2005)
Chen, Y., Luh, P.B., Guan, C., Zhao, Y., Michel, L.D., Coolbeth, M.A.: Short-term load forecas-ting: similar day-based wavelet neural network. IEEE Trans. on Power Systems 25, 322–330 (2010)
Australian Energy Market Opeartor (AEMO) http://www.aemo.com.au
Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural Networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems 16(1), 44–55 (2001)
Darbellay, G.A., Slama, M.: Forecasting the short-term demand for electricity - Do neural networks stand a better chance? International Journal of Forecasting 16, 71–83 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koprinska, I., Rana, M., Agelidis, V.G. (2012). Electricity Load Forecasting: A Weekday-Based Approach. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-33266-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33265-4
Online ISBN: 978-3-642-33266-1
eBook Packages: Computer ScienceComputer Science (R0)