Short-Term Load Forecasting Using a Novel Deep Learning Framework

Xiaoyu Zhang, Rui Wang, Tao Zhang, Yajie Liu and Yabing Zha
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Xiaoyu Zhang: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Rui Wang: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Tao Zhang: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Yajie Liu: College of System Engineering, National University of Defense Technology, Changsha 410073, China
Yabing Zha: College of System Engineering, National University of Defense Technology, Changsha 410073, China

Energies, 2018, vol. 11, issue 6, 1-15

Abstract: 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.

Keywords: short-term load forecasting; a novel deep learning framework; deep belief network; restricted Boltzmann machine; Elman neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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