Title :
Air Conditioning Load Prediction Based on DE-SVM Algorithm
Author :
Chen, Zhonghai ; Sun, Yong ; Yang, Guoli ; Wu, Tengfei ; Li, Guizhu ; Xin, Longbiao
Author_Institution :
Dept. of Urban Constr., Hebei Inst. of Archit. & Civil Eng., Zhangjiakou, China
Abstract :
Based on SVM (Support Vector Machine) theory, and the model to predict air conditioning load was established. In order to optimize the behavior of SVM, the DE (Differential Evolution) algorithm was introduced into classic SVM. The DE-SVM model is applied to a real example. The comparisons between the predicted results of the three models-GA (Genetic Algorithm) model, ACO (Ant Colony Optimization) and POS (Partial Swarm Optimization) show the precision of the DE-SVM algorithm is high with the maximum relative error being 2.52%.
Keywords :
air conditioning; differential equations; evolutionary computation; genetic algorithms; load forecasting; particle swarm optimisation; power engineering computing; support vector machines; ACO; DE-SVM algorithm; POS; air conditioning load prediction; ant colony optimization; differential evolution algorithm; genetic algorithm; maximum relative error; partial swarm optimization; support vector machine; Air conditioning; Ant colony optimization; Civil engineering; Information technology; Kernel; Linear regression; Mathematical model; Prediction algorithms; Predictive models; Support vector machines; Air Conditioning load; DE-SVM; Prediction;
Conference_Titel :
Intelligent Information Technology and Security Informatics (IITSI), 2010 Third International Symposium on
Conference_Location :
Jinggangshan
Print_ISBN :
978-1-4244-6730-3
Electronic_ISBN :
978-1-4244-6743-3
DOI :
10.1109/IITSI.2010.41
