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Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach

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  • Deo, Ravinesh C.
  • Şahin, Mehmet
  • Adamowski, Jan F.
  • Mi, Jianchun

Abstract

Global advocacy to mitigate climate change impacts on pristine environments, wildlife, ecology, and health has led scientists to design technologies that harness solar energy with remotely sensed, freely available data. This paper presents a study that designed a regionally adaptable and predictively efficient extreme learning machine (ELM) model to forecast long-term incident solar radiation (ISR) over Australia. The relevant satellite-based input data extracted from the Moderate Resolution Imaging Spectroradiometer (i.e., normalized vegetation index, land-surface temperature, cloud top pressure, cloud top temperature, cloud effective emissivity, cloud height, ozone and near infrared-clear water vapour), enriched by geo-temporal input variables (i.e., periodicity, latitude, longitude and elevation) are applied for a total of 41 study sites distributed approximately uniformly and paired with ground-based ISR (target). Of the 41 sites, 26 are incorporated in an ELM algorithm for the design of a universal model, and the remainder are used for model cross-validation. A universally-trained ELM (with training data as a global input matrix) is constructed, and the spatially-deployable model is applied at 15 test sites. The optimal ELM model is attained by trial and error to optimize the hidden layer activation functions for feature extraction and is benchmarked with competitive artificial intelligence algorithms: random forest (RF), M5 Tree, and multivariate adaptive regression spline (MARS). Statistical metrics show that the universally-trained ELM model has very good accuracy and outperforms RF, M5 Tree, and MARS models. With a distinct geographic signature, the ELM model registers a Legates & McCabe's Index of 0.555–0.896 vs. 0.411–0.858 (RF), 0.434–0.811 (M5 Tree), and 0.113–0.868 (MARS). The relative root-mean-square (RMS) error of ELM is low, ranging from approximately 3.715–7.191% vs. 4.907–10.784% (RF), 7.111–11.169% (M5 Tree) and 4.591–18.344% (MARS). Taylor diagrams that illustrate model preciseness in terms of RMS centred difference, error analysis, and boxplots of forecasted vs. observed ISR also confirmed the versatility of the ELM in generating forecasts over heterogeneous, remote spatial sites. This study ascertains that the proposed methodology has practical implications for regional energy modelling, particularly at national scales by utilizing remotely-sensed satellite data, and thus, may be useful for energy feasibility studies at future solar-powered sites. The approach is also important for renewable energy exploration in data-sparse or remote regions with no established measurement infrastructure but with a rich and viable satellite footprint.

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  • Deo, Ravinesh C. & Şahin, Mehmet & Adamowski, Jan F. & Mi, Jianchun, 2019. "Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 235-261.
    • Handle: RePEc:eee:rensus:v:104:y:2019:i:c:p:235-261
    DOI: 10.1016/j.rser.2019.01.009
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    6. AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Adamowski, Jan F. & Li, Yan, 2019. "Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    7. Chen, Jiang & Zhu, Weining & Yu, Qian, 2021. "Estimating half-hourly solar radiation over the Continental United States using GOES-16 data with iterative random forest," Renewable Energy, Elsevier, vol. 178(C), pages 916-929.
    8. Sujan Ghimire & Ravinesh C Deo & Nawin Raj & Jianchun Mi, 2019. "Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction," Energies, MDPI, vol. 12(12), pages 1-39, June.
    9. Eini, Mohammad Reza & Salmani, Haniyeh & Piniewski, Mikołaj, 2023. "Comparison of process-based and statistical approaches for simulation and projections of rainfed crop yields," Agricultural Water Management, Elsevier, vol. 277(C).
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    11. Sungwon Kim & Meysam Alizamir & Nam Won Kim & Ozgur Kisi, 2020. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
    12. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    13. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).
    14. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2023. "Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches," Applied Energy, Elsevier, vol. 339(C).
    15. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    16. Preeti Verma & Sunil Patil, 2023. "A Machine Learning Approach and Methodology for Solar Radiation Assessment Using Multispectral Satellite Images," Annals of Data Science, Springer, vol. 10(4), pages 907-932, August.
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