Thermal Load Prediction by Various Hybrid Models Based on Different Artificial Intelligence Techniques

摘要

In order to enhance the accuracy of building load prediction. This paper proposed a hybrid model based on time-indexed autoregressive with exogenous meteorologically inputs (ARX) in which the coefficients are estimated through a second order weighted least squares support vector regression(LS-SVM). This model is combined with Support Vector Machine Regression (SVMR) developed with Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DE). Hybrid methods named PSO-LSSVM and DE-LSSVM, both meteorological and historical information into consideration fundamentally. These proposed methods are tested on a medium-sized office-building. As a result, the accuracy of hybrid prediction models has the better performance compared to that of other prediction methods in predicting Cooling load and Heating load.