Paper Title:
Building Cooling Load Forecasting Based on Support Vector Machines with Simulated Annealing
  Abstract

Accurate forecasting of building cooling load has been one of the most important issues in the electricity industry. Recently, along with energy-saving optimal control, accurate forecast of electricity load has received increasing attention. Because of the general nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting electricity load. Support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. In order to improve time efficiency of prediction, a new hourly cooling load prediction model and method based on Support Vector Machine in this paper. Moreover, simulated annealing (SA) algorithms were employed to choose the parameters of a SVM model. Subsequently, examples of cooling load data from Guangzhou were used to illustrate the proposed SVM-SA model. A comparison of the performance between SVM optimized by Particle Swarm Optimization (SVM-PSO) and SVM-SA is carried out. Experiments results demonstrate that SVM-SA can achieve better accuracy and generalization than the SVM-PSO. Consequently, the SVM-SA model provides a promising alternative for forecasting building load.

  Info
Periodical
Advanced Materials Research (Volumes 108-111)
Edited by
Yanwen Wu
Pages
1003-1008
DOI
10.4028/www.scientific.net/AMR.108-111.1003
Citation
X. M. Li, L. X. Ding, J. H. Lǔ, lan L. Li, "Building Cooling Load Forecasting Based on Support Vector Machines with Simulated Annealing", Advanced Materials Research, Vols. 108-111, pp. 1003-1008, 2010
Online since
May 2010
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Fei Fei Gao, Ji Chen Fang, Qiang Zhang, Qin Zhang, Zhan Gen Wang, Meng Zhu Shi
Abstract:This paper establishes a combination forecasting model based on Radia Basis Function Neural Network (RBFNN). It puts forward a seeking...
515
Authors: Xi Miao Jia, Guo Ping Song, Ting Wang, Feng Kong
Abstract:Due to the variety and the randomicity of its influencing factors, the electricity demand forecasting is a difficult problem for a long time....
2983
Authors: Wei Shen, Yue Shi Sun
Abstract:Electric load forecasting is an important aspect in the operation of energy market. Many researchers have tried various methods and have...
1225
Authors: Fang Xiao
Chapter 11: Disaster Prevention and Mitigation
Abstract:Forest coverage prediction based on least squares support vector regression algorithm is presented in the paper.Forest coverage data of...
2978
Authors: Xiao Mei Zhu, Qun Yan Zhang
Chapter 3: Information Technology and Engineering
Abstract:To improve the accuracy of prediction on software failure data, one combine forecasting model is proposed based on least square support...
542