A Short Term Load Forecasting Algorithm Based on Gray Elman Neural Network and Genetic Algorithm

DOI

10.2991/icmmita-15.2015.102How to use a DOI?

Keywords

gray theory; genetic algorithm; Elman neural network; load forecasting

Abstract

Short term power load sample is highly variable, and the influence factors are not determined, the data sample is little. In view of the characteristics of the load, we combine the Grey Theory and Elman neural network to predict the short-term power load. Because the gray neural network convergence is slow, We introduce the genetic algorithm to the gray Elman neural network optimization, and propose the genetic algorithm to optimize the gray Elman neural network algorithm, the genetic algorithm to optimize the gray Elman neural network algorithm is applied to short-term load forecast. Experimental results show that the prediction accuracy is improved. The algorithm achieves fast convergence, and it is feasible and effective.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Baoyi Wang
AU  - Zheng Wang
AU  - Shaomin Zhang
PY  - 2015/11
DA  - 2015/11
TI  - A Short Term Load Forecasting Algorithm Based on Gray Elman Neural Network and Genetic Algorithm
BT  - Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 526
EP  - 531
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-15.2015.102
DO  - 10.2991/icmmita-15.2015.102
ID  - Wang2015/11
ER  -