Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model

Tao, Hai and Sharafati, Ahmad and Mohammed, Achite and Salih, Sinan Q. and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Al-Ansari, Nadhir and Yaseen, Zaher Mundher (2020) Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model. IEEE Access, 8. pp. 12026-12042.

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Abstract

Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information’s are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model’s estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm-2 compared to 4.24 and 3.24 Wm-2 (MLR) and 8.33 and 5.37 Wm-2 (ARIMA).


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Date Deposited: 05 Feb 2020 02:41
Last Modified: 28 Jul 2020 04:50
Uncontrolled Keywords: energy feasibility studies, extreme learning machine, solar energy estimation, multivariate modeling, solar energy mapping
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
05 Environmental Sciences > 0502 Environmental Science and Management > 050205 Environmental Management
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
Identification Number or DOI: 10.1109/ACCESS.2020.2965303
URI: http://eprints.usq.edu.au/id/eprint/37807

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