Abstract
The estimation of baseline electricity consumptions for energy efficiency and load management measures is an essential issue. When implementing real-time energy management platforms for Automatic Monitoring and Targeting (AMT) of energy consumption, baselines shall be calculated previously and must be adaptive to sudden changes. Short Term Load Forecasting (STLF) techniques can be a solution to determine a pertinent frame of reference. In this study, two different forecasting methods are implemented and assessed: a first method based on load curve clustering and a second one based on signal decomposition using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). Both methods were applied to three different sets of data corresponding to three different industrial sites from different sectors across France. For the evaluation of the methods, a specific criterion adapted to the context of energy management is proposed. The obtained results are satisfying for both of the proposed approaches but the clustering based method shows a better performance. Perspectives for exploring different forecasting methods for these applications are considered for future works, as well as their application to different load curves from diverse industrial sectors and equipments.
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References
Alhourani, F., & Saxena, U. (2009). Factors affecting the implementation rates of energy and productivity recommendations in small and medium sized companies. Journal of Manufacturing Systems, 28(1), 41–45. doi:10.1016/j.jmsy.2009.04.001.
Attik, M., Bougrain, L., & Alexandre, F. (2005). Self-organizing map initialization. In Artificial neural networks: biological inspirations – ICANN 2005, Warsaw (pp. 357–362).
Bunn, D. W., & Farmer, E. D. (1985). Comparative models for electrical load forecasting. Chichester/New York: Wiley.
Bunse, K., Vodicka, M., Schönsleben, P., Brülhart, M., & Ernst, F. O. (2011). Integrating energy efficiency performance in production management – Gap analysis between industrial needs and scientific literature. Journal of Cleaner Production, 19(6–7), 667–679. doi:10.1016/j.jclepro.2010.11.011.
Chicco, G. (2012). Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy, 42(1), 68–80. doi:10.1016/j.energy.2011.12.031.
Chicco, G., Napoli, R., & Piglione, F. (2006). Comparisons among clustering techniques for electricity customer classification. IEEE Transactions on Power Systems, 21(2), 933–940. doi:10.1109/TPWRS.2006.873122.
Cottrell, M. (2003). Some other applications of the SOM algorithm: How to use the Kohonen algorithm for forecasting. In Invited lecture at the international work-conference on artificial neural networks, IWANN 2003: Maó, Menorca, Spain.
Coughlin, K., Piette, M. A., Goldman, C., & Kiliccote, S. (2009). Statistical analysis of baseline load models for non-residential buildings. Energy and Buildings, 41(4), 374–381.
Daultrey, S. (1976). Principal components analysis. Norwich: Geo Abstracts.
Fidalgo, J. N., Matos, M. A., & Ribeiro, L. (2012). A new clustering algorithm for load profiling based on billing data. Electric Power Systems Research, 82(1), 27–33. doi:10.1016/j.epsr.2011.08.016.
Giacone, E., & Manc, S. (2012). Energy efficiency measurement in industrial processes. Energy, 38(1), 331–345. doi:10.1016/j.energy.2011.11.054.
Goldberg, M. L., & Kennedy Agnew, G. (2003). Protocol development for demand response calculation: Findings and recommendations (Technical report). KEMA-Xenergy.
Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009). Electric load forecasting methods: Tools for decision making. European Journal of Operational Research, 199(3), 902–907
Hatcher, L. (1994). A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary: Sas Institute.
Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems, 16(1), 44–55.
Hu, S., Liu, F., He, Y., & Hu, T. (2012). An on-line approach for energy efficiency monitoring of machine tools. Journal of Cleaner Production, 27, 133–140. doi:10.1016/j.jclepro.2012.01.013.
Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464–1480. doi:10.1109/5.58325.
Lendasse, A., Lee, J., Wertz, V., & Verleysen, M. (2002). Forecasting electricity consumption using nonlinear projection and self-organizing maps. Neurocomputing, 48(1), 299–311.
Li, D. C., Chang, C. J., Chen, C. C., & Chen, W. C. (2012). Forecasting short-term electricity consumption using the adaptive grey-based approach – An Asian case. Special Issue on Forecasting in Management Science, 40(6), 767–773. doi:10.1016/j.omega.2011.07.007.
Mahmoudi-Kohan, N., Moghaddam, M. P., & Sheikh-El-Eslami, M. (2010). An annual framework for clustering-based pricing for an electricity retailer. Electric Power Systems Research, 80(9), 1042–1048. doi:10.1016/j.epsr.2010.01.010.
Manera, M., & Marzullo, A. (2005). Modelling the load curve of aggregate electricity consumption using principal components. Environmental Modelling & Software, 20(11), 1389–1400. doi:10.1016/j.envsoft.2004.09.019.
McLachlan, G. J. (2004). Discriminant analysis and statistical pattern recognition. Hoboken: Wiley-Interscience.
Räsänen, T., Voukantsis, D., Niska, H., Karatzas, K., & Kolehmainen, M. (2010). Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data. Applied Energy, 87(11), 3538–3545. doi:10.1016/j.apenergy.2010.05.015.
Reichl, J., & Kollmann, A. (2010). Strategic homogenisation of energy efficiency measures: An approach to improve the efficiency and reduce the costs of the quantification of energy savings. Energy Efficiency, 3(3), 189–201.
Rousset, P. (1999). Applications des algorithmes d’auto-organisation à la classification et à la prévision. PhD thesis, Université Paris I, Paris.
Soliman, S. Ah., & Al-Kandari, A. M. (2010). Electrical load forecasting: Modeling and model construction. New York: Elsevier
Taylor, J. W., De Menezes, L. M., & McSharry, P. E. (2006) A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting, 22(1), 1–16.
Thang, K., Aggarwal, R., McGrail, A., & Esp, D. (2003). Analysis of power transformer dissolved gas data using the self-organizing map. IEEE Transactions on Power Delivery, 18(4), 1241–1248. doi:10.1109/TPWRD.2003.817733.
Tsekouras, G., Kotoulas, P., Tsirekis, C., Dialynas, E., & Hatziargyriou, N. (2008). A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers. Electric Power Systems Research, 78(9), 1494–1510. doi:10.1016/j.epsr.2008.01.010.
Vijayaraghavan, A., & Dornfeld, D. (2010). Automated energy monitoring of machine tools. CIRP Annals Manufacturing Technology, 59(1), 21–24. doi:10.1016/j.cirp.2010.03.042.
Vine, E. (2008). Breaking down the silos: The integration of energy efficiency, renewable energy, demand response and climate change. Energy Efficiency, 1(1), 49–63.
Vine, E. L., & Sathaye, J. A. (2000). The monitoring, evaluation, reporting, verification, and certification of energy-efficiency projects. Mitigation and Adaptation Strategies for Global Change, 5(2), 189–216.
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Blancarte, J., Batton-Hubert, M., Bay, X., Girard, MA., Grau, A. (2015). Short Term Load Forecasting in the Industry for Establishing Consumption Baselines: A French Case. In: Antoniadis, A., Poggi, JM., Brossat, X. (eds) Modeling and Stochastic Learning for Forecasting in High Dimensions. Lecture Notes in Statistics(), vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-18732-7_1
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DOI: https://doi.org/10.1007/978-3-319-18732-7_1
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