Next Article in Journal
Power Decoupling of a Single Phase DC-AC Dual Active Bridge Converter Based on an Integrated Bidirectional Buck/Boost Stage
Previous Article in Journal
A Reconfiguration Method for Extracting Maximum Power from Non-Uniform Aging Solar Panels
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Energies 2018, 11(10), 2744; https://doi.org/10.3390/en11102744

Short-Term Load Interval Prediction Using a Deep Belief Network

College of System Engineering, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Received: 6 September 2018 / Revised: 28 September 2018 / Accepted: 4 October 2018 / Published: 13 October 2018
(This article belongs to the Section Electrical Power and Energy System)
Full-Text   |   PDF [4287 KB, uploaded 13 October 2018]   |  

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. View Full-Text
Keywords: deep belief network; lower upper bound estimation method; short-term load prediction; interval predication deep belief network; lower upper bound estimation method; short-term load prediction; interval predication
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhang, X.; Shu, Z.; Wang, R.; Zhang, T.; Zha, Y. Short-Term Load Interval Prediction Using a Deep Belief Network. Energies 2018, 11, 2744.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top