A Short Term Load Forecasting Algorithm Based on Gray Elman Neural Network and Genetic Algorithm
- 10.2991/icmmita-15.2015.102How to use a DOI?
- gray theory; genetic algorithm; Elman neural network; load forecasting
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.
- © 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/).
Cite this article
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 -