Vapnik's learning theory applied to energy consumption forecasts in residential buildings
Résumé
For the purpose of energy conservation, we present in this paper an introduction to the use of Support Vector (SV) Learning Machines used as a data mining tool applied to buildings energy consumption data from a measurement campaign. Experiments using a SVM-based software tool for the prediction of the electrical consumption of a residential building is performed. The data included one year and three months of daily recordings of electrical consumption and climate data such as temperatures and humidities. The learning stage was done for a first part of the data, the predictions were done for the last month. Performances of the model and contributions of significant factors were also derived. The results show good performances for the model. Besides the second experiment consists in model re-estimations on a one-year daily recording dataset lagged at one-day time intervals in such a way that we derive temporal series of influencing factors weights along with model performance criteria. Finally we introduce a perturbation in one of the influencing variable to detect a model change. Comparing contributing weights with and without the perturbation, the sudden contributing weight change could have diagnosed the perturbation. The important point is the ease of the production of many models. This method announces future research work in the exploitation of possibilities of this "model factory".
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