Particle Swarm Optimization-based LS-SVM for Building Cooling Load Prediction

Accurate predicting of building cooling load has been one of the most important issues in the energy-saving building, which provides an approach to integrate and optimize the heating, ventilating, and air-conditioning (HVAC) system cooling supply system efficiently. Because of the remarkable nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting building cooling load, but suffer from the phenomena of local minimum and over-fitting. This paper investigates the feasibility of using Least Squares Support vector regression (LS-SVR) to forecast building cooling load. LS-SVR is a novel type of learning machine, which has been successfully employed to solve nonlinear regression and time series problems. Due to the importance of parameters optimization in LS-SVR model, particle swarm optimization (PSO) was used to optimize the model parameters. The experiment results show that PSO can quickly obtain the optimal parameters satisfying the precision requirement with a simple calculation, which solves the problem of complex calculation and empiricism in conventional methods. The evaluation on the testing cases shows the SVR model with PSO has a good generalization performance and can be a promising alternative for building cooling load prediction.