Short-Term Load Interval Prediction Using a Deep Belief Network

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

Energies, 2018, vol. 11, issue 10, 1-18

Abstract: In load predication, point-based forecasting methods have been widely applied. However, uncertainties arising in load predication bring significant challenges for such methods. This therefore drives the development of new methods amongst which interval predication is one of the most effective. In this study, a deep belief network-based lower–upper bound estimation (LUBE) approach is proposed, and a genetic algorithm is applied to reinforce the search ability of the LUBE method, instead of simulated an annealing algorithm. The approach is applied to the short-term load prediction on some realistic electricity load data. To demonstrate the effectiveness and efficiency of the proposed method, it is compared with three state-of-the-art methods. Experimental results show that the proposed approach can significantly improve the predication accuracy.

Keywords: deep belief network; lower upper bound estimation method; short-term load prediction; interval predication (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 (3)

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