Publication Date:
2018-06-15
Description:
Energies, Vol. 11, Pages 1554: Short-Term Load Forecasting Using a Novel Deep Learning Framework Energies doi: 10.3390/en11061554 Authors: Xiaoyu Zhang Rui Wang Tao Zhang Yajie Liu Yabing Zha Short-term load forecasting is the basis of power system operation and analysis. In recent years, the use of a deep belief network (DBN) for short-term load forecasting has become increasingly popular. In this study, a novel deep-learning framework based on a restricted Boltzmann machine (RBM) and an Elman neural network is presented. This novel framework is used for short-term load forecasting based on the historical power load data of a town in the UK. The obtained results are compared with an individual use of a DBN and Elman neural network. The experimental results demonstrate that our proposed model can significantly ameliorate the prediction accuracy.
Electronic ISSN:
1996-1073
Topics:
Energy, Environment Protection, Nuclear Power Engineering
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