Using connectionist systems for electric energy consumption forecasting in shopping centers
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A solution is presented for the short-term electrical energy forecasting in shopping centers located in the Netherlands and Belgium. A forecasting method is proposed on the basis of connectionist systems. General description of the forecasting method is provided, as well as its specific features with respect to the forecasting problem are studied. Several connectionist models are generalized, stated and applied, notably, moving average model, linear regression model, and neural network model. In addition, changes in forecasting quality are demonstrated depending on different input variables. The results of using these connectionist models are discussed, and conclusions regarding specific features of every model are outlined.
KeywordsEnergy Consumption Remote Control Linear Regression Model Forecast Error Mean Absolute Percentage Error
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- 1.Bol’shov, L.A., Kanevskii, M.F., Savel’eva, E.A., Timonin, V.A., and Chernov, S.Yu., Electric Energy Consumption Forecasting: Modern Approaches and Example of Examination, Izv. Akad. Nauk, Energetika, 2004, no. 6, pp. 74–93.Google Scholar
- 2.Melamed, M.A., Modern Analysis and Design Methods for Energy Consumption Modes in Power Systems, in Itogi nauki i tekhniki. Energeticheskie sistemy i ikh avtomatizatsiya (Scientific and Engineering Results. Energy Systems and Their Automation), 1988, vol. 4, pp. 4–11.Google Scholar
- 3.EcoSCADA: Data Acquisition System for Electric Energy Consumption, http://www.ecoscada.com.
- 4.Shcherbakov, M.V. and Shcherbakova, N.L., Comparing Approximation Models of Dimensionless Radial Flow Velocity of Liquids in Rotating Curvilinear Channels, Izv. Volgograd. Tekhn. Univ., 2007, no. 2, pp. 32–33.Google Scholar
- 5.Shcherbakov, M.V., ICDMS-Software as a Service Based on Connectionist Systems for Identification, Izv. Volgograd. Tekhn. Univ., 2009, vol. 12, no. 7, pp. 88–91.Google Scholar
- 6.Brebels, A., Shcherbakov, M.V., Kamaev, V.A., et al., Mathematical and Statistical Framework for Comparison of Neural Network Models with Other Algorithms for Prediction of Energy Consumption in Shopping Centers, Proc. 37 Int. Conf. Information Technology in Science, Education, Telecommunication and Business, Yalta-Gurzuf, 2010, pp. 96–97.Google Scholar
- 7.Frawley, W.J., Patetsky-Shapiro, G., and Mathews, C.J., Knowledge Discovery in Databases: An Overview, Cambridge: AAAI/MIT Press, 1991.Google Scholar
- 8.Kasabov, N., Evolving Connectionists Systems. The Knowledge Engineering Approach, London: Springer, 2007.Google Scholar
- 10.Zhao, H.T. and Maclennan, J., Data Mining with SQL Server 2005, New York: Wiley, 2005.Google Scholar