自然科学版 英文版
自然科学版 英文版
自然科学版 英文版

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中南大学学报(英文版)

Journal of Central South University

Vol. 18    No. 4    August 2011

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Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine
HE Yong-xiu(何永秀), HE Hai-ying(何海英), WANG Yue-jin(王跃锦), LUO Tao(罗涛)

School of Economics and Management, North China Electric Power University, Beijing 102206, China

Abstract:Firstly, general regression neural network (GRNN) was used for variable selection of key influencing factors of residential load (RL) forecasting. Secondly, the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning. In addition, the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory. Then, the model of PSO-Bayes least squares support vector machine (PSO-Bayes-LS-SVM) was established. A case study was then provided for the learning and testing. The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%, respectively. At last, taking a specific province RL in China as an example, the forecast results of RL from 2011 to 2015 were obtained.

 

Key words: residential load; load forecasting; general regression neural network (GRNN); evidence theory; PSO-Bayes least squares support vector machine

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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