Proceedings ArticleDOI
Estimation of solar radiation on horizontal and inclined surfaces in Sfax, TUNISIA
Ismail Baklouti,Zied Driss,Mohamed Abid +2 more
- pp 131-140
TLDR
In this article, the Liu and Jordon model was used to estimate the hourly global, diffuse and direct solar radiations for horizontal surfaces and the total daily solar radiation on inclined and vertical surfaces in the region of Sfax, Tunisia.Abstract:
This paper has been made to estimate the solar radiation on horizontal and inclined surfaces in Sfax, Tunisia. The model developed in this communication can be used to estimate the hourly global, diffuse and direct solar radiations for horizontal surfaces and the total daily solar radiation on inclined and vertical surfaces in the region of Sfax. Moreover, the method presented here can be used to evaluate the energy production in Sfax of photovoltaic projects like water pumping solar, on-grid applications and off-grid applications. The estimation method of the hourly and daily solar radiations used the Liu and Jordon model. In addition, the values of monthly of average daily solar radiation on a horizontal surface are taken from NASA, Surface meteorology and Solar Energy. The mounting position of solar panel is assumed to be facing towards the south of Sfax. The present results are comparable with results of the PVGIS (Photovoltaic Geographical Information System).read more
Citations
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Journal ArticleDOI
UV and solar photo-degradation of naproxen: TiO2 catalyst effect, reaction kinetics, products identification and toxicity assessment
TL;DR: Direct photolysis and TiO2-photocatalytic degradation of naproxen (NPX) in aqueous solution were studied using a UV lamp and solar irradiation to find the degradation was found to be in accordance with pseudo-first order kinetics, the photoc atalytic process being more efficient thanphotolysis.
Journal ArticleDOI
Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data
Christil Pasion,Torrey J. Wagner,Clay Michael Koschnick,Steven J. Schuldt,Jada B. Williams,Kevin P. Hallinan +5 more
TL;DR: This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations and found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling.
Journal ArticleDOI
Small-scale solar plants coupled with smart public transport system and its coordination with the grid
TL;DR: In this paper, small-scale solar plants (SPs) coupled with smart public transport system and its coordination with the grid have been presented, where electric buses (EBs) are used for mass transportation and charged from the energy points provided through the ring road of Guwahati city, Assam, India.
Journal ArticleDOI
Numerical study of the greenhouse solar drying of olive mill wastewater under different conditions
Chaima Bouraoui,Faycal Ben Nejma +1 more
TL;DR: In this article, the authors developed thermal modeling of the olive mill wastewater drying process in a greenhouse solar dryer, and a configuration was proposed and simulated using the commercial sof...
Modeling Power Output of Horizontal Solar Panels Using Multivariate Linear Regression and Random Forest Machine Learning
Abstract: United States Air Force energy resiliency goals are aimed to increase renewable energy implementation among its facilities. Researchers at the Air Force Institute of Technology designed, manufactured, and distributed 37 photovoltaic test systems to Air Force installations around the world. This research uses two types of modeling techniques, multivariate linear regression and random forest machine learning, to determine which technique will better predict power output for horizontal solar panels. Many previous solar panel prediction studies use solar irradiation data as an input. This study does not use irradiation as an input and aims to predict power output with input variables that are more readily available. If power output of a horizontal solar panel can be predicted using available weather data, then assessing the possibility of utilizing horizontal panels in any global location becomes possible. Input variables used for each model was latitude, month, hour, ambient temperature, humidity, wind speed, cloud ceiling, and altitude. The variance each model accounted was used as a comparison measure. The multivariate linear regression model accounted for 56.2% of the variance in a sample validation dataset. The random forest machine learning model accounted for 65.8% variance. The random forest model outperformed the multivariate linear regression model by accounting for 9.6% more variance. The most important variable in reducing the random forest model mean squared error was the month of the year, closely followed by cloud ceiling. Wind speed was the least important variable in reducing model error. More predictor variables are needed to increase predictability of horizontal solar panel power output if irradiation is not present as an input.
References
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