A parallel statistical learning approach to the prediction of building energy consumption based on large datasets

H. X. Zhao (1) , F. Magoules (1)

1 MAS - Mathématiques Appliquées aux Systèmes - EA 4037

The prediction of future energy consumption of buildings based on historical performances is an important approach to achieve energy efficiency. A simulation method is here introduced to obtain sufficient clean historical consumption data to improve the accuracy of the prediction. The widely used statistical learning method, Support Vector Machines (SVMs), is then applied to train and to evaluate the prediction model. Due to the time-consuming problem of the training process, a parallel approach is applied to improve the speed of the training of large amounts of data when considering multiple buildings. The experimental results show very good performance of this model and of the parallel approach, allowing the application of Support Vector Machines on more complex problems of energy efficiency involving large datasets.

Mots clés: Parallel Computing; Support Vector Machines (SVMs); Prediction; Model; Energy Efficiency; Parallel Computing.

Domaines: Ingénierie, finance et science [cs.CE]; Intelligence artificielle [cs.AI]; Calcul parallèle, distribué et partagé [cs.DC]

H. X. Zhao, F. Magoules. A parallel statistical learning approach to the prediction of building energy consumption based on large datasets. 6th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), Oct 2009, Wuhan, Hubei, China. ⟨hal-00617926⟩