MARS model for prediction of short- and long-term global solar radiation
Edited book (chapter)
Chapter Title | MARS model for prediction of short- and long-term global solar radiation |
---|---|
Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 1821 |
Book Title | Predictive modelling for energy management and power systems engineering |
Authors | Balalla, Dilki T. (Author), Nguyen-Huy, Thong (Author) and Deo, Ravinesh (Author) |
Editors | Deo, Ravinesh, Samui, Pijush and Roy, Sanjiban Sekhar |
Page Range | 391-436 |
Chapter Number | 13 |
Number of Pages | 46 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Amsterdam, Netherlands |
ISBN | 9780128177723 |
Web Address (URL) | https://www.elsevier.com/books/predictive-modelling-for-energy-management-and-power-systems-engineering/deo/978-0-12-817772-3 |
Abstract | The intention of this research was to try and address the research question 'Is machine learning algorithm, Multivariate Adaptive Regression Splines model, a versatile forecasting model for solar radiation?' The objective of this chapter is to develop a machine learning (ML) algorithm to validate and assess errors for the method used to forecast solar radiation based on historical data. The specific aims are to construct (1) short-term (daily) global solar radiation model using the MARS algorithm considering the nonlinear behavior of surface-level solar radiation with its predictor variables; and (2) long-term (monthly) global solar radiation model using the MARS algorithm to enable the solar energy assessment over a long-term period and considering. This chapter carried out short-term and long-term solar radiation forecasting model development for regional Queensland. Short-term forecasting provides predictions up to 7 days ahead. These forecasts are valuable for grid operators in order to make important decisions for grid operation. It will provide valuable information regarding the time scheduling of power systems (Wan et al., 2015). Long-term forecasting has been carried out considering 1-month ahead, 3-month, and 6-month ahead forecast. This is useful for energy companies to make decisions and negotiate contracts with energy producers (Martı´n et al., 2010) and also for effective operation and maintenance planning of solar power systems (Koca et al., 2011). The information gathered from the seasonal analysis can be used for studying the seasonal patterns of the solar energy and for Seasonal Thermal Energy Storage (i.e., STES) (Allen et al., 1984) where the heat acquired from solar collectors in hot months can be stored for future use when needed, including during winter months. |
Keywords | solar energy; forecasting |
ANZSRC Field of Research 2020 | 419999. Other environmental sciences not elsewhere classified |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Sciences |
Centre for Applied Climate Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5yq5/mars-model-for-prediction-of-short-and-long-term-global-solar-radiation
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Ghimire, Sujan, Deo, Ravinesh C, Casillas-Perez, David and Salcedo-sanz, Sancho. 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction." Renewable Energy. 190, pp. 408-424. https://doi.org/10.1016/j.renene.2022.03.120Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model
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Ghimire, Sujan, Bhandari, Binayak, Casillas-Perez, David, Deo, Ravinesh C. and Salcedo-sanz, Sancho. 2022. "Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia." Engineering Applications of Artificial Intelligence. 112, pp. 1-26. https://doi.org/10.1016/j.engappai.2022.104860Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms
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Castillo-Boton, C., Casillas-Perez, D., Casanova-Mateo, C., Ghimire, S., Cerro-Prada, E., Gutierrez, P. A., Deo, R. C. and Salcedo-sanz, S.. 2022. "Machine learning regression and classification methods for fog events prediction." Atmospheric Research. 272, pp. 1-23. https://doi.org/10.1016/j.atmosres.2022.106157Quantum Artificial Intelligence Predictions Enhancement by Improving Signal Processing
Riaz, Farina, Abdulla, Shahab, Ni, Wei, Radfar, Mohsen, Deo, Ravinesh and Hopkins, Susan. 2022. "Quantum Artificial Intelligence Predictions Enhancement by Improving Signal Processing." Quantum Australia Conference 2022. Online 23 - 25 Feb 2022 Toowoomba, Australia. https://doi.org/10.13140/RG.2.2.34754.66245A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar
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Lu, Zhixiang, Feng, Qi, Wei, Yongping, Zhao, Yan, Deo, Ravinesh C., Xie, Jiali, Zhou, Sha, Zhu, Meng and Xu, Min. 2022. "Basin management inspiration from impacts of alternating dry and wet conditions on water production and carbon uptake in Murray-Darling Basin." Science of the Total Environment. 851 (Part 2), pp. 1-8. https://doi.org/10.1016/j.scitotenv.2022.158359Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model
Ahmed, Abul Abrar Masrur, Deo, Ravinesh C., Ghimire, Sujan, Downs, Nathan J., Devi, Aruna, Barua, Prabal D. and Yaseen, Zaher M.. 2022. "Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model." Sustainability. 14 (17), pp. 1-27. https://doi.org/10.3390/su141711070Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors
Ahmed, A. A. Masrur, Sharma, Ekta, Jui, S. Janifer Jabin, Deo, Ravinesh C., Nguyen-Huy, Thong and Ali, Mumtaz. 2022. "Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors." Remote Sensing. 14 (5), pp. 1-24. https://doi.org/10.3390/rs14051136Cloud Affected Solar UV Predictions with Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System
Prasad, Salvin S., Deo, Ravinesh C., Downs, Nathan, Igoe, Damien, Parisi, Alfio V. and Soar, Jeffrey. 2022. "Cloud Affected Solar UV Predictions with Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System." IEEE Access. 10, pp. 24704-24720. https://doi.org/10.1109/ACCESS.2022.3153475Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data
Jayasinghe, W. J. M. Lakmini Prarthana, Deo, Ravinesh C., Ghahramani, Afshin, Ghimire, Sujan and Raj, Nawin. 2022. "Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data." Journal of Hydrology. 607, pp. 1-19. https://doi.org/10.1016/j.jhydrol.2022.127534Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects
Bhagat, Suraj Kumar, Tiyasha, Tiyasha, Kumar, Adarsh, Malik, Tabarak, Jawad, Ali H., Khedher, Khaled Mohamed, Deo, Ravinesh C. and Yaseen, Zaher Mundher. 2022. "Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects." Journal of Environmental Management. 309, pp. 1-16. https://doi.org/10.1016/j.jenvman.2022.114711An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis
Al-Hadeethi, Hanan, Abdulla, Shahab, Diykh, Mohammed, Deo, Ravinesh C. and Green, Jonathan H.. 2022. "An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis." Frontiers in Neuroinformatics. 15, pp. 1-15. https://doi.org/10.3389/fninf.2021.808339Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results
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Diykh, Mohammed, Miften, Firas Sabar, Abdulla, Shahab, Deo, Ravinesh C., Siuly, Siuly, Green, Jonathan H. and Oudah, Atheer Y.. 2022. "Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals." Measurement. 190 (110731), pp. 1-13. https://doi.org/10.1016/j.measurement.2022.110731General equilibrium impact evaluation of food top-up induced by households’ renewable power self-supply in 141 regions
Nguyen, Duong Binh, Nong, Duy, Simshauser, Paul and Nguyen-Huy, Thong. 2022. "General equilibrium impact evaluation of food top-up induced by households’ renewable power self-supply in 141 regions." Applied Energy. 306 (Part B), pp. 1-13. https://doi.org/10.1016/j.apenergy.2021.118126Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data
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Nguyen-Huy, Thong. 2020. "Copula-based statistical modelling of synoptic-scale climate indices for quantifying and managing agricultural risks in Australia." Bulletin of the Australian Mathematical Society. 101 (1), pp. 166-169. https://doi.org/10.1017/S0004972719001217Drought Outlook Products Review
Cobon, David, Nguyen-Huy, Thong and Reardon-Smith, Kate. 2019. Drought Outlook Products Review. Toowoomba, Australia. University of Southern Queensland.Domino effect of climate change over two millennia in ancient China’s Hexi Corridor
Feng, Qi, Yan, Linshan, Deo, Ravinesh C., AghaKouchak, Amir, Adamowski, Jan F., Stone, Roger, Yin, Zhenliang, Liu, Wei, Si, Jianhua, Wen, Xiaohu, Zhu, Meng and Cao, Shixiong. 2019. "Domino effect of climate change over two millennia in ancient China’s Hexi Corridor." Nature Sustainability. 2, pp. 957-961. https://doi.org/10.1038/s41893-019-0397-9Bat algorithm for dam–reservoir operation
Ethteram, Mohammad, Mousavi, Sayed-Farhad, Karami, Hojat, Farzin, Saeed, Deo, Ravinesh, Othman, Faridah Binti, Chau, Kwok-Wing, Sarkamaryan, Saeed, Singh, Vijay P. and El-Shafie, Ahmed. 2018. "Bat algorithm for dam–reservoir operation." Environmental Earth Sciences. 77 (13), pp. 1-15. https://doi.org/10.1007/s12665-018-7662-5Characteristics of ecosystem water use efficiency in a desert riparian forest
Ma, Xiaohong, Feng, Qi, Su, Yonghong, Yu, Tengfei and Deo, Ravinesh C.. 2018. "Characteristics of ecosystem water use efficiency in a desert riparian forest." Environmental Earth Sciences. 77 (358). https://doi.org/10.1007/s12665-018-7518-zThe influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River
Diop, Lamine, Bodian, Ansoumana, Djaman, Koffi, Yaseen, Zaher Mundher, Deo, Ravinesh C., El-Shafie, Ahmed and Brown, Larry C.. 2018. "The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River." Environmental Earth Sciences. 77 (5). https://doi.org/10.1007/s1266Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River
Raheli, Bahare, Aalami, Mohammad Taghi, El-Shafie, Ahmed, Ghorbani, Mohammad Ali and Deo, Ravinesh C.. 2017. "Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River." Environmental Earth Sciences. 76 (14). https://doi.org/10.1007/s12665-017-6842-zMethodology for producing the Drought Monitor
Pudmenzky, Christa, Guillory, Laura, Cobon, David H. and Nguyen-Huy, Thong. 2020. Methodology for producing the Drought Monitor. Toowoomba, Queensland. University of Southern Queensland.Northern Australia Climate Program: supporting adaptation in rangeland grazing systems through more targeted climate forecasts, improved drought information and an innovative extension program
Cobon, David, Jarvis, Chelsea, Reardon-Smith, Kate, Guillory, Laura, Pudmenzky, Christa, Nguyen-Huy, Thong, Mushtaq, Shahbaz and Stone, Roger. 2021. "Northern Australia Climate Program: supporting adaptation in rangeland grazing systems through more targeted climate forecasts, improved drought information and an innovative extension program." The Rangeland Journal. 43 (3), pp. 87-100. https://doi.org/10.1071/RJ20074Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots
Sharma, Ekta, Deo, Ravinesh C., Soar, Jeffrey, Prasad, Ramendra, Parisi, Alfio V. and Raj, Nawin. 2022. "Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots." Atmospheric Environment. 279, pp. 1-13. https://doi.org/10.1016/j.atmosenv.2022.119111New double decomposition deep learning methods for river water level forecasting
Ahmed, A. A. Masrur, Deo, Ravinesh C., Ghahramani, Afshin, Feng, Qi, Raj, Nawin, Yin, Zhenliang and Yang, Linshan. 2022. "New double decomposition deep learning methods for river water level forecasting." Science of the Total Environment. 831, pp. 1-21. https://doi.org/10.1016/j.scitotenv.2022.154722Global disparities in agricultural climate index-based insurance research
Adeyinka, Adewuyi Ayodele, Kath, Jarrod, Nguyen-Huy, Thong, Mushtaq, Shahbaz, Souvignet, Maxime, Range, Matthias and Barratt, Jonathan. 2022. "Global disparities in agricultural climate index-based insurance research." Climate Risk Management. 35, pp. 1-15. https://doi.org/10.1016/j.crm.2022.100394Drought outlook validation using ACCESS-S2 hindcast
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