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Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model
Computer Science Department, Baoji University of Arts and Sciences, Baoji, China.
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Faculty of Nature and Life Sciences, Laboratory of Water and Environment, University Hassiba Benbouali Chlef, Hay Es-Salem Chlef, Algeria.
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
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2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 12026-12042Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
USA: IEEE, 2020. Vol. 8, p. 12026-12042
Keywords [en]
Energy feasibility studies, extreme learning machine, solar energy estimation, multivariate
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-77489DOI: 10.1109/ACCESS.2020.2965303OAI: oai:DiVA.org:ltu-77489DiVA, id: diva2:1387928
Note

Validerad;2020;Nivå 2;2020-01-23 (johcin)

Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-01-23Bibliographically approved

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