Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network

Huynh, Anh Ngoc‐Lan and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and An-Vo, Duc-Anh ORCID: https://orcid.org/0000-0001-7528-7139 and Ali, Mumtaz and Raj, Nawin and Abdulla, Shahab ORCID: https://orcid.org/0000-0002-1193-6969 (2020) Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network. Energies, 13 (14):3517.

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Abstract

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license http://creativecommons.org/licenses/by/4.0/)
Faculty/School / Institute/Centre: Current - USQ College (8 Jun 2020 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Date Deposited: 16 Jul 2020 03:44
Last Modified: 29 Jul 2020 04:41
Uncontrolled Keywords: solar radiation; long short-term memory network; near real-time solar radiation forecasting
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
05 Environmental Sciences > 0502 Environmental Science and Management > 050299 Environmental Science and Management not elsewhere classified
Socio-Economic Objectives (2008): C Society > 93 Education and Training > 9399 Other Education and Training > 939999 Education and Training not elsewhere classified
Identification Number or DOI: 10.3390/en13143517
URI: http://eprints.usq.edu.au/id/eprint/39034

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