New parallel support vector regression for predicting building energy consumption

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

1 MAS - Mathématiques Appliquées aux Systèmes - EA 4037

One challenge of predicting building energy consump- tion is to accelerate model training when the dataset is very large. This paper proposes an efficient parallel implementation of support vector regression based on decomposition method for solving such problems. The parallelization is performed on the most time-consuming work of training, i.e., to update the gradient vector f . The inner problems are dealt by sequential minimal optimization solver. The underlying parallelism is conducted by the shared memory version of Map-Reduce paradigm, making the system particularly suitable to be applied to multi-core and multiprocessor systems. Experimental results show that our implementation offers a high speed increase compared to Libsvm, and it is superior to the state-of-the-art MPI implementation Pisvm in both speed and storage requirement.

Mots clés: Support Vector Regression (SVR); Building energy consumption; Parallel computing; Multi-core; Map-Reduce

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

H. X. Zhao, F. Magoules. New parallel support vector regression for predicting building energy consumption. IEEE Symposium Series on Computational Intelligence (SSCI 2011), Apr 2011, Paris, France. ⟨hal-00617935⟩