Vapnik's learning theory applied to energy consumption forecasts in residential buildings

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 1 year and 3 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 and 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. The second experiment consists of model re-estimations on a 1-year daily recording dataset lagged at 1-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 variables 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'.