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Machine learning methods are widely studied and applied to predict building energy consumption. Since the factors associated with building energy behaviors are quite abundant and complex, this paper investigates for the first time how the selection of subsets of features influence the model performance when statistical learning method is adopted to derive the model. In this paper the optimal features are selected based on the feasibility of obtaining them and on the scores they provide under the evaluation of some filter methods. The selected subset is then evaluated on three data sets by support vector regression involving two kernel functions: radial basis function and polynomial function. Experimental results confirm the validity of the selected subset and show that the proposed feature selection method can guarantee the prediction accuracy and reduces the computational time for data analyzing.

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Abstract-This paper proposes a new and efficient parallel implementation of support vector machines based on decomposition method for handling large scale datasets. The parallelizing is performed on the most time-and-memory consuming work of training, i.e., to update the vector f . The inner problems are dealt by sequential minimal optimization solver. Since the underlying parallelism is realized by the shared memory version of Map-Reduce paradigm, our system is easy to build and particularly suitable to apply to multi-core and multiprocessor systems. Experimental results show that on most of the tested datasets, our system offers higher than fourfold increase in speed compared to Libsvm, and it is also far more efficient than the MPI implementation Pisvm.