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Wavelet-Gaussian process regression model for forecasting daily solar radiation in the Saharan climate

Clean Energy 2021年第2期5卷 316-328页

作者： Khaled Ferkous Farouk Chellali Abdalah Kouzou Belgacem Bekkar LAADI Laboratory Department of Electrical EngineeringZian Achour UniversityDjelfaAlgeria University of Ghardaia GhardaiaAlgeria

Forecasting solar radiation is fundamental to several domains related to renewable energy where several methods have been used to predict daily solar radiation,such as artificial intelligence and hybrid ***,the Gaussian process regression(GPR)algorithm has been used successfully in remote sensing and Earth *** this paper,a wavelet-coupled Gaussian process regression(W-GPR)model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia(Algeria).For this purpose,3 years of data(2013-15)have been used in model training while the data of 2016 were used to validate the *** this work,different types of mother wavelets and different combinations of input data were evaluated based on the minimum air temperature,relative humidity and extraterrestrial solar radiation on a horizontal *** results demonstrated the effectiveness of the new hybrid W-GPR model compared with the classical GPR model in terms of root mean square error(RMSE),relative root mean square error(rRMSE),mean absolute error(MAE)and determination coefficient(R^(2)).

关键词： Gaussian process regression wavelets hybrid models forecasting solar radiation solar measurements Ghardaia

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Efficient utilization of multi-core processors and many-core co-processors on supercomputer beacon for scalable geocomputation and geo-simulation over big earth data

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Big Earth Data 2018年第1期2卷 65-85页

作者： Chenggang Lai Xuan Shi Miaoqing Huang Department of Computer Science and Computer Engineering University of ArkansasFayettevilleARUSA Department of Geosciences University of ArkansasFayettevilleARUSA

Digital earth science data originated from sensors aboard satellites and platforms such as airplane,UAV,and mobile systems are increasingly available with high spectral,spatial,vertical,and temporal resolution *** such big earth science data are processed and analyzed via geocomputation solutions,or utilized in geospatial simulation or modeling,considerable computing power and resources are necessary to complete the *** classic computer clusters equipped by central processing units(CPUs)and the new computing resources of graphics processing units(GPUs)have been deployed in handling big earth data,coprocessors based on the Intel’s Many Integrated Core(MIC)Architecture are emerging and adopted in many high-performance computer *** paper introduces how to efficiently utilize Intel’s Xeon Phi multicore processors and MIC coprocessors for scalable geocomputation and geo-simulation by implementing two algorithms,Maximum Likelihood Classification(MLC)and Cellular Automata(CA),on supercomputer Beacon,a cluster of *** different programming models are examined,including(1)the native model,(2)the offload model,(3)the symmetric model,and(4)the hybrid-offload *** can be concluded that while different kinds of parallel programming models can enable big data handling efficiently,the hybrid-offload model can achieve the best performance and *** different programming models can be applied and extended to other types of geocomputation to handle big earth data.

关键词： MIC native model offload model hybrid models Maximum Likelihood Classification Cellular Automata

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