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Energies 2018, 11(6), 1554; https://doi.org/10.3390/en11061554

Short-Term Load Forecasting Using a Novel Deep Learning Framework

College of System Engineering, National University of Defense Technology, Changsha 410073, China
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Received: 17 April 2018 / Revised: 31 May 2018 / Accepted: 5 June 2018 / Published: 14 June 2018
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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. View Full-Text
Keywords: short-term load forecasting; a novel deep learning framework; deep belief network; restricted Boltzmann machine; Elman neural network short-term load forecasting; a novel deep learning framework; deep belief network; restricted Boltzmann machine; Elman neural network
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Zhang, X.; Wang, R.; Zhang, T.; Liu, Y.; Zha, Y. Short-Term Load Forecasting Using a Novel Deep Learning Framework. Energies 2018, 11, 1554.

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