Two-Stage Approach for Electricity Consumption Forecasting in Public Buildings
Many preprocessing and prediction techniques have been used for large-scale electricity load forecasting. However, small-scale prediction, such as in the case of public buildings, has received little attention. This field presents certain specific features. The most distinctive one is that consumption is extremely influenced by the activity in the building. For that reason, a suitable approach to predict the next 24-hour consumption profiles is presented in this paper. First, the features that influence the consumption are processed and selected. These environmental variables are used to cluster the consumption profiles in subsets of similar behavior using neural gas. A direct forecasting approach based on Support Vector Regression (SVR) is applied to each cluster to enhance the prediction. The input vector is selected from a set of past values. The approach is validated on teaching and research buildings at the University of León.
KeywordsTime-series prediction electricity consumption forecasting feature selection clustering regression
Unable to display preview. Download preview PDF.
- 3.Sideratos, G., Vitellas, I., Hatziargyriou, N.: A load forecasting hybrid method for an isolated power system. In: 2011 16th International Conference on Intelligent System Application to Power Systems (ISAP), pp. 1–5 (September 2011)Google Scholar
- 4.Cao, L.: Support vector machines experts for time series forecasting. Neurocomputing 51(0), 321–339 (2003)Google Scholar
- 5.Weigend, A.S., Gershenfeld, N.A.: Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading (1993)Google Scholar
- 6.Gupta, P., Yamada, K.: Adaptive short-term forecasting of hourly loads using weather information. IEEE Transactions on PAS-91(5), 2085–2094 (1972)Google Scholar
- 12.Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI 1995), vol. 2 (1995)Google Scholar
- 14.Sorjamaa, A., Hao, J., Lendasse, A.: Mutual Information and k-Nearest Neighbors Approximator for Time Series Prediction. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 553–558. Springer, Heidelberg (2005)Google Scholar
- 15.Müller, K., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting time series with Support Vector Machines. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997)Google Scholar
- 18.Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM toolbox for Matlab 5 (2000)Google Scholar