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

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  • Anh Ngoc-Lan Huynh

    (School of Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Darling Heights, QLD 4350, Australia)

  • Ravinesh C. Deo

    (School of Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Darling Heights, QLD 4350, Australia)

  • Duc-Anh An-Vo

    (Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

  • Mumtaz Ali

    (Deakin-SWU Joint Research Centre on Big Data, School of Information Technology, Deakin University, Burwood, VIC 2134, Australia)

  • Nawin Raj

    (School of Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Darling Heights, QLD 4350, Australia)

  • Shahab Abdulla

    (Open Access College, University of Southern Queensland, Darling Heights, QLD 4350, Australia)

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).

Suggested Citation

  • Anh Ngoc-Lan Huynh & Ravinesh C. Deo & Duc-Anh An-Vo & Mumtaz Ali & Nawin Raj & Shahab Abdulla, 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network," Energies, MDPI, vol. 13(14), pages 1-30, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3517-:d:381918
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