Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network
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...
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MDPI AG
2020-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/14/3517 |
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| author | Anh Ngoc-Lan Huynh Ravinesh C. Deo Duc-Anh An-Vo Mumtaz Ali Nawin Raj Shahab Abdulla |
| author_facet | Anh Ngoc-Lan Huynh Ravinesh C. Deo Duc-Anh An-Vo Mumtaz Ali Nawin Raj Shahab Abdulla |
| author_sort | Anh Ngoc-Lan Huynh |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-10T18:37:02Z |
| format | Article |
| id | doaj.art-e8233f4251f4490293899d151dafd605 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:37:02Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e8233f4251f4490293899d151dafd6052023-11-20T06:12:08ZengMDPI AGEnergies1996-10732020-07-011314351710.3390/en13143517Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory NetworkAnh Ngoc-Lan Huynh0Ravinesh C. Deo1Duc-Anh An-Vo2Mumtaz Ali3Nawin Raj4Shahab Abdulla5School of Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Darling Heights, QLD 4350, AustraliaSchool of Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Darling Heights, QLD 4350, AustraliaCentre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD 4350, AustraliaDeakin-SWU Joint Research Centre on Big Data, School of Information Technology, Deakin University, Burwood, VIC 2134, AustraliaSchool of Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Darling Heights, QLD 4350, AustraliaOpen Access College, University of Southern Queensland, Darling Heights, QLD 4350, AustraliaThis 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).https://www.mdpi.com/1996-1073/13/14/3517solar radiationlong short-term memory networknear real-time solar radiation forecasting |
| spellingShingle | Anh Ngoc-Lan Huynh Ravinesh C. Deo Duc-Anh An-Vo Mumtaz Ali Nawin Raj Shahab Abdulla Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network Energies solar radiation long short-term memory network near real-time solar radiation forecasting |
| title | Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network |
| title_full | Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network |
| title_fullStr | Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network |
| title_full_unstemmed | Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network |
| title_short | Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network |
| title_sort | near real time global solar radiation forecasting at multiple time step horizons using the long short term memory network |
| topic | solar radiation long short-term memory network near real-time solar radiation forecasting |
| url | https://www.mdpi.com/1996-1073/13/14/3517 |
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