Novel short-term solar radiation hybrid model: long short-term memory network integrated with robust local mean decomposition

Huynh, Anh Ngoc-Lan and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Ali, Mumtaz and Abdulla, Shahab ORCID: https://orcid.org/0000-0002-1193-6969 and Raj, Nawin (2021) Novel short-term solar radiation hybrid model: long short-term memory network integrated with robust local mean decomposition. Applied Energy, 298:117193. pp. 1-19. ISSN 0306-2619


Abstract

Data-intelligent algorithms tailored for short-term energy forecasting can generate meaningful information on the future variability of solar energy developments. Traditional forecasting methods find it relatively difficult to obtain a reliable solar energy monitoring system because of the inherent nonlinearities in solar radiation and the related atmospheric input variables to any forecasting system. This paper proposes a new artificial intelligence-based hybrid model by employing the robust version of local mean decomposition (RLMD) and Long Short-term Memory (LSTM) network denoted as RLMD-LSTM. The objective model (i.e., RLMD-LSTM) is built near real-time, half-hourly ground-based solar radiation dataset for the solar rich, metropolitan study sites in Vietnam with all of the forecasting results being benchmarked through classical modelling approaches (i.e., Support Vector Regression SVR, Long Short-term Memory LSTM, Multivariate Adaptive Regression Spline MARS, Persistence) as well as the other alternative hybrid methods (i.e., RLMD-MARS, RLMD-Persistence and RLMD-SVR). Verified by statistical metrics and visual infographics, the present results demonstrate that the proposed model can generate satisfactory predictions, outperforming several counterpart methods. The predictive performance is stable for all study sites that the root-mean-square error remained profoundly lower for RLMD-LSTM (19–20%) compared with RLMD-MARS (20–21%), RLMD-SVR (29–35%), RLMD- Persistence (29–51%), LSTM (25–48%), MARS (21–51%) and SVR (23–85%), Persistence (29–51%). The Legates and McCabe’s Index, yielding a value of approximately 0.7988–0.9256 for RLMD-LSTM compared with 0.765–0.8142, 0.4917–0.5711, 0.6900–0.7482, 0.6914–0.7646, 0.4349–0.7170 respectively, for the RLMD-MARS, RLMD-SVR, RLMD-Persistence, LSTM, MARS, SVR, Persistence models, also confirms the outstanding performance of RLMD-LSTM model. Accordingly, the study ascertains that the newly designed approach can be a potential candidate for real-time energy management, renewable energy integration into a power grid and other decisions to optimise the overall system's scheduling and performance.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - USQ College (8 Jun 2020 -)
Faculty/School / Institute/Centre: Current - Institute for Advanced Engineering and Space Sciences (1 Aug 2018 -)
Date Deposited: 04 Jun 2021 05:49
Last Modified: 14 Jul 2021 06:55
Uncontrolled Keywords: short-term solar radiation prediction, Robust local mean decomposition, deep learning
Fields of Research (2008): 05 Environmental Sciences > 0599 Other Environmental Sciences > 059999 Environmental Sciences not elsewhere classified
Fields of Research (2020): 41 ENVIRONMENTAL SCIENCES > 4199 Other environmental sciences > 419999 Other environmental sciences not elsewhere classified
Socio-Economic Objectives (2008): D Environment > 96 Environment > 9603 Climate and Climate Change > 960399 Climate and Climate Change not elsewhere classified
Socio-Economic Objectives (2020): 18 ENVIRONMENTAL MANAGEMENT > 1899 Other environmental management > 189999 Other environmental management not elsewhere classified
Funding Details:
Identification Number or DOI: https://doi.org/10.1016/j.apenergy.2021.117193
URI: http://eprints.usq.edu.au/id/eprint/42134

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