Parallel Support Vector Machines Applied to the Prediction of Multiple Buildings Energy Consumption

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

1 MAS - Mathématiques Appliquées aux Systèmes - EA 4037

Analyzing the energy performance in a building is an important task in energy conservation. To accurately predict the energy consumption is difficult in practice since the building is a complex system with many parameters involved. To obtain enough historical data of energy uses and to find out an approach to analyze them become mandatory. In this paper, we propose a simulation method with the aim of obtaining energy data for multiple buildings. Support vector machines method with Gaussian kernel is applied to obtain the prediction model. For the first time, a parallel implementation of support vector machines is used to accelerate the model training process. Our experimental results show very good performance of this approach, paving the way for further applications of support vector machines method on large energy consumption datasets.

Keywords: Energy consumption; building; prediction; support vector machines; parallel computing

Domains: Computational Engineering, Finance, and Science [cs.CE]; Artificial Intelligence [cs.AI]; Distributed, Parallel, and Cluster Computing [cs.DC]

H. X. Zhao, F. Magoules. Parallel Support Vector Machines Applied to the Prediction of Multiple Buildings Energy Consumption. Journal of Algorithms and Computational Technology, 2010, 4 (2), pp.231-250. ⟨hal-00617930⟩