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• W2954174953 endingPage "109247" @default.
• W2954174953 startingPage "109247" @default.
• W2954174953 abstract "The accurate prediction of global solar radiation (GSR) with remote sensing in metropolitan, regional and remote, yet solar-rich sites, is a core requisite for cleaner energy utilization, monitoring and conversion of renewable energy into usable power. Data-driven models that investigate the feasibility of solar-fueled energies, face challenges in respect to identifying their appropriate input data as such variables may not be available at all sites due to a lack of environmental monitoring system. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived predictors are employed to train three-phase hybrid SVR model for monthly GSR prediction. Firstly, to acquire relevant model input features, MODIS variables are screened with the Particle Swarm Optimization (PSO) algorithm, and secondly, a Gaussian emulation method of sensitivity analysis is incorporated on all screened variables to ascertain their relative role in predicting GSR. To address pertinent issues of non-stationarities, PSO selected variables are decomposed with Maximum Overlap Discrete Wavelet Transformation prior to its incorporation in Support Vector Regression (SVR), constructing a three-phase PSO-W-SVR hybrid model where the hyper-parameters are acquired by evolutionary (i.e., PSO & Genetic Algorithm) and Grid Search methods. Three-phase PSO-W-SVR hybrid model is benchmarked with alternative machine learning models. Thirty-nine model scenarios are formulated: 13 without feature selection (e.g., SVR), 13 with feature selection (e.g., PSO-SVR for two-phase models) and the remainder 13 with feature selection strategy coupled with data decomposition algorithm (e.g., PSO-W-SVR leading to a three-phase model). Metrics such as skill score (RMSESS), root mean square error (RMSE), mean absolute error (MAE), Willmott’s (WI), Legates & McCabe’s (E1) and Nash–Sutcliffe coefficients (ENS) are applied to comprehensively evaluate prescribed models. Empirical results register high performance of three-phase hybrid PSO-W-SVR models, exceeding the prescribed alternative models. High predictive ability evidenced by a low RRMSE and high E1 ascertains PSO-W-SVR hybrid model as considerably favorable in its capability to be enriched by MODIS satellite-derived variables. Maximum Overlap Discrete Wavelet Transform algorithm is also seen to provide resolved patterns in satellite variables, leading to a superior performance compared to the other data-driven model. The research avers that a three-phase hybrid PSO-W-SVR model can be a viable tool to predict GSR using satellite derived data as predictors, and is particularly useful for exploration of renewable energies where satellite footprint are present but regular environmental monitoring systems may be absent." @default.
• W2954174953 created "2019-07-12" @default.
• W2954174953 creator A5049303725 @default.
• W2954174953 creator A5065141057 @default.
• W2954174953 creator A5075682713 @default.
• W2954174953 creator A5091890459 @default.
• W2954174953 date "2019-10-01" @default.
• W2954174953 modified "2023-10-03" @default.
• W2954174953 title "Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction" @default.
• W2954174953 cites W1149231055 @default.
• W2954174953 cites W1179171804 @default.
• W2954174953 cites W1471436312 @default.
• W2954174953 cites W1543715688 @default.
• W2954174953 cites W1598241312 @default.
• W2954174953 cites W1764874023 @default.
• W2954174953 cites W18678914 @default.
• W2954174953 cites W1969930237 @default.
• W2954174953 cites W1981374261 @default.
• W2954174953 cites W1984926525 @default.
• W2954174953 cites W1993717606 @default.
• W2954174953 cites W2002404570 @default.
• W2954174953 cites W2002674327 @default.
• W2954174953 cites W2004630602 @default.
• W2954174953 cites W2008697060 @default.
• W2954174953 cites W2013355544 @default.
• W2954174953 cites W2017732481 @default.
• W2954174953 cites W2033904036 @default.
• W2954174953 cites W2035876214 @default.
• W2954174953 cites W2037460094 @default.
• W2954174953 cites W2039455461 @default.
• W2954174953 cites W2044451649 @default.
• W2954174953 cites W2044562303 @default.
• W2954174953 cites W2046156160 @default.
• W2954174953 cites W2047223952 @default.
• W2954174953 cites W2047884674 @default.
• W2954174953 cites W2062087947 @default.
• W2954174953 cites W2064675550 @default.
• W2954174953 cites W2074088705 @default.
• W2954174953 cites W2079316304 @default.
• W2954174953 cites W2086350890 @default.
• W2954174953 cites W2102148524 @default.
• W2954174953 cites W2108591703 @default.
• W2954174953 cites W2125107816 @default.
• W2954174953 cites W2125520565 @default.
• W2954174953 cites W2134829952 @default.
• W2954174953 cites W2150010190 @default.
• W2954174953 cites W2195527334 @default.
• W2954174953 cites W2198869818 @default.
• W2954174953 cites W2262639697 @default.
• W2954174953 cites W2274744025 @default.
• W2954174953 cites W2318515285 @default.
• W2954174953 cites W2394860501 @default.
• W2954174953 cites W2398936495 @default.
• W2954174953 cites W2464017710 @default.
• W2954174953 cites W2505879376 @default.
• W2954174953 cites W2518301555 @default.
• W2954174953 cites W2532294903 @default.
• W2954174953 cites W2560323025 @default.
• W2954174953 cites W2581811121 @default.
• W2954174953 cites W2587088850 @default.
• W2954174953 cites W2615467640 @default.
• W2954174953 cites W2627156000 @default.
• W2954174953 cites W2730816529 @default.
• W2954174953 cites W2734401432 @default.
• W2954174953 cites W2750787742 @default.
• W2954174953 cites W2751979836 @default.
• W2954174953 cites W2756625063 @default.
• W2954174953 cites W2757997884 @default.
• W2954174953 cites W2766387448 @default.
• W2954174953 cites W2766566007 @default.
• W2954174953 cites W2766998427 @default.
• W2954174953 cites W2767559196 @default.
• W2954174953 cites W2771963500 @default.
• W2954174953 cites W2772433304 @default.
• W2954174953 cites W2781009421 @default.
• W2954174953 cites W2790160110 @default.
• W2954174953 cites W2799674728 @default.
• W2954174953 cites W2806583606 @default.
• W2954174953 cites W2886806372 @default.
• W2954174953 cites W2902071146 @default.
• W2954174953 cites W2911964244 @default.
• W2954174953 cites W2912697538 @default.
• W2954174953 cites W2914530224 @default.
• W2954174953 cites W3018770027 @default.
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• W2954174953 doi "https://doi.org/10.1016/j.rser.2019.109247" @default.
• W2954174953 hasPublicationYear "2019" @default.
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