Building Energy Load Prediction By Using Ls-Svm
This paper presents a least square support vector machine (LS-SVM) model to predict the heating and cooling loads of a building based on simulation data for building energy performance. The dataset used in this study include the overall height, relative compactness, surface area, wall area, roof area, orientation, glazing area, and glazing area distribution of building. By using these variables as inputs, heating and cooling loads of building are estimated. There are a lot of machine learning methods such as artificial neural networks (ANN) for this purpose in the literature. We investigate the performance of LS-SVM as an estimator, which is a modified version of support vector machines (SVM). According to obtained results, it is shown that the proposed LS-SVM based method can predict heating and cooling loads of building with a very high accuracy rate.
Keywordsó Heating Load, Cooling Load, Least Square Support Vector Machines.