Abstract
After the electricity liberalization in Europe, the electricity market moved to a more competitive supply market with higher efficiency in power production. As a result of this competitiveness, accurate models for forecasting long-term power consumption become essential for electric utilities as they help operating and planning of the utility’s facilities including Transmission and Distribution (T&D) equipments. In this paper, we develop a multi-step statistical analysis approach to interpret the correlation between power consumption of residential as well as industrial buildings and its main potential driving factors using the dataset of the Irish Commission for Energy Regulation (CER). In addition we design a hybrid model for forecasting long-term daily power consumption on the scale of portfolio of buildings using the models of conditional inference trees and linear regression. Based on an extensive evaluation study, our model outperforms two robust machine learning algorithms, namely random forests (RF) and conditional inference tree (ctree) algorithms in terms of time efficiency and prediction accuracy for individual buildings as well as for a portfolio of buildings. The proposed model reveals that dividing buildings in homogeneous groups, based on their characteristics and inhabitants demographics, can increase the prediction accuracy and improve the time efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aman, S., Frincu, M., Chelmis, C., Noor, M.U.: Empirical Comparison of Prediction Methods for Electricity Consumption Forecasting, Department of Computer Science, University of Southern California, Los Angeles, CA, 90089 (2012)
Aman, S., Simmhan, Y., Prasanna, V.: Improving energy use forecast for campus micro-grids using indirect indicators, December 2011
Breiman, L.: Random forests. Mach. Learn. J. 45, 5–32 (2001)
Commission for Energy Regulation (CER): Electricity Smart Metering Technology Trials Findings Report. ESB Networks, Belgard Square North, Tallaght, Dublin 24 (2011)
Fan, S., Hyndman, R.: Short-term load forecasting based on a semi-parametric additive model. IEEE Trans. Power Syst. 27(1), 134–141 (2012)
German, G.: Smoothing and non-parametric regression. Int. J. Syst. Sci. (2001)
Hong, T., Gui, M., Baran, M., Willis, H.: Modeling and forecasting hourly electric load by multiple linear regression with interactions. In: 2010 IEEE Power and Energy Society General Meeting, pp. 1–8, July 2010
Hothorn, T., Hornik, K., Zeileis, A.: Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15(3), 651–674 (2006)
Jiang, H., Lee, Y., Liu, F.: Anomaly Detection, Forecasting and Root Cause Analysis of Energy Consumption for a Portfolio of Buildings Using Multi-step statistical Modeling, US Patent App. 13/098,044 (2012)
Coughlin, K., Piette, M.A., Goldman, C., Kiliccote, S.: Estimating demand response load impacts: evaluation of baseline load models for nonresidential buildings in California. Lawrence Berkeley National Lab, Technical report LBNL-63728 (2008)
Metaxiotis, K., Kagiannas, A., Askounis, D., Psarras, J.: Artificial intelligence in short-term electric load forecasting: a state-of-the-art survey for the researcher. Energy Convers. Manag. 44, 1525–1534 (2003)
Khotanzad, A., Afkhami-Rohani, R., Lu, T.L., Abaye, A., Davis, M., Maratukulam, D.: Annstlf-a neural-network-based electric load forecasting system. IEEE Trans. Neural Netw. 8(4), 835–846 (1997)
Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)
Alvarez, F.M., Troncoso, A., Riquelme, J., Ruiz, J.A.: Energy time series forecasting based on pattern sequence similarity. IEEE Trans. Knowl. Data Eng. 23(8), 1230–1243 (2011)
Mori, H., Takahashi, A.: Hybrid intelligent method of relevant vector machine and regression tree for probabilistic load forecasting. In: 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe), pp. 1–8, December 2011
Rui, Y., El-Keib, A.: A review of ann-based short-term load forecasting models. In: 1995 Proceedings of the Twenty-Seventh Southeastern Symposium on System Theory, pp. 78–82, March 1995
Shen, W., Babushkin, V., Aung, Z., Woon, W.: An ensemble model for day-ahead electricity demand time series forecasting. In: Proceedings of the Fourth International Conference on Future Energy Systems, pp. 51–62. ACM, New York (2013)
Silipo, R., Winters, P.: Big Data, Smart Energy, and Predictive Analytics Time Series Prediction of Smart Energy Data (2013). http://www.knime.org
Wan, S., Yu, X.-H.: Facility power usage modeling and short term prediction with artificial neural networks. In: Zhang, L., Lu, B.-L., Kwok, J. (eds.) ISNN 2010. LNCS, vol. 6064, pp. 548–555. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13318-3_68
Yang, X.: Comparison of the LS-SVM based load forecasting models. In: 2011 International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT), vol. 6, pp. 2942–2945, August 2011
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Alkhatib, W., Alhamoud, A., Böhnstedt, D., Steinmetz, R. (2017). Hybrid Model for Large Scale Forecasting of Power Consumption. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_57
Download citation
DOI: https://doi.org/10.1007/978-3-319-59153-7_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59152-0
Online ISBN: 978-3-319-59153-7
eBook Packages: Computer ScienceComputer Science (R0)