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Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Huynh, Anh Ngoc-Lan, Deo, Ravinesh C., An-Vo, Duc-Anh, Ali, Mumtaz, Raj, Nawin and Abdulla, Shahab 2020, Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network, Energies, vol. 13, no. 14, pp. 1-28, doi: 10.3390/en13143517.

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Title Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network
Author(s) Huynh, Anh Ngoc-Lan
Deo, Ravinesh C.
An-Vo, Duc-Anh
Ali, MumtazORCID iD for Ali, Mumtaz orcid.org/0000-0002-6975-5159
Raj, Nawin
Abdulla, Shahab
Journal name Energies
Volume number 13
Issue number 14
Article ID 3517
Start page 1
End page 28
Total pages 28
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2020
ISSN 1996-1073
1996-1073
Keyword(s) Science & Technology
Technology
Energy & Fuels
solar radiation
long short-term memory network
near real-time solar radiation forecasting
SUPPORT VECTOR REGRESSION
NEURAL-NETWORKS
SUNSHINE DURATION
HYBRID METHOD
MODEL
IRRADIANCE
PREDICTION
ERROR
GENERATION
WIND
Summary 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).
Language eng
DOI 10.3390/en13143517
Indigenous content off
Field of Research 02 Physical Sciences
09 Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30140971

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.