Prediction of building energy consumption using an improved real coded genetic algorithm based least squares support vector machine approach

The least-squares support vector machine (LSSVM) strategy has played a crucial role in the forecasting of building energy consumption owing to its remarkable nonlinear mapping capabilities in prediction. In order to build an effective LSSVM method, its two free parameters, the regularization parameter and the kernel parameter, must be selected carefully. However, LSSVM using a conventional real-coded genetic algorithm (RCGA) or differential evolution algorithm (DEA) for determining the aforementioned two parameters consumes excessive amounts of computation time. In this study, a novel LSSVM for effective prediction of daily building energy consumption is designed by utilizing a hybrid of the direct search optimization (DSO) algorithm and RCGA, called the DSORCGA. The proposed DSORCGA differs from the conventional RCGA in terms of the reproduction operator and the crossover operator, and is used to optimize free parameters of LSSVM for faster computation speed and higher predictive accuracy. Finally, in a MATLAB2010a environment, actual building energy consumption data are adopted to run the proposed DSORCGA-LSSVM and conventional RCGA-LSSVM and DEA-LSSVM. Further, the simulation results in the target period are compared with those of actual recorded energy consumption data, and improvement in computation time is revealed via numerical simulation.