Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model

• Published in: IEEE access Vol. 8; pp. 12026 - 12042
• Main Authors: Hai, Tao, Sharafati, Ahmad, Mohammed, Achite, Salih, Sinan Q., Deo, Ravinesh C., Al-Ansari, Nadhir, Yaseen, Zaher Mundher
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Autoregressive models; Computational modeling; Covariance; Datasets; Energy feasibility studies; Estimation; extreme learning machine; Geoteknik; Machine learning; Mathematical model; multivariate; multivariate modeling; Neurons; Prediction models; Predictive models; Regression analysis; Root-mean-square errors; Soil Mechanics; Solar energy; solar energy estimation; solar energy mapping; Solar radiation; Statistical analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2965303

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).