کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
8110523 1522290 2018 16 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
Evaluating the effect of air pollution on global and diffuse solar radiation prediction using support vector machine modeling based on sunshine duration and air temperature
ترجمه فارسی عنوان
ارزیابی تأثیر آلودگی هوا بر پیش بینی تابش خورشیدی جهان و پراکنده با استفاده از مدل سازی دستگاه بردار بر اساس مدت زمان نور آفتاب و دمای هوا
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موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
Increasing air pollutants attenuate surface solar radiation, and thus can be influential variables for solar radiation prediction. In this study, six air pollutants of PM2.5, PM10, SO2, NO2, CO and O3 as well as air quality index (AQI) were chosen for analyzing their single and integrated effects on daily global and diffuse solar radiation (Rs and Rd) prediction. Seven single air pollution parameters, 15 combinations of two parameters and 20 combinations of three parameters were considered using Support Vector Machine (SVM) based on sunshine duration or air temperature. Daily meteorological and air pollution data between January 2014 and December 2015 from China's capital city of Beijing were used to train SVM models and data from January 2016 to December 2016 for testing. Results show that AQI was the most relevant air pollution parameter for both Rs and Rd prediction, followed by O3 for Rs and by PM2.5 for Rd with slight difference as that of AQI. The combination of PM10 and O3 and the combination of PM2.5 and O3 were the most influential combination of two air pollution inputs for Rs and Rd prediction, respectively. The combination of PM2.5, PM10 and O3 was the most optimal combination of three air pollution inputs for both daily Rs and Rd prediction. Compared with SVM models without considering air pollution, the accuracy of SVM models with the most influential combinations of one, two and three air pollution inputs was improved by 13.9%, 19.8% and 22.2% in terms of RMSE for sunshine-based Rs, respectively. The corresponding values were 15.2%, 22.0% and 22.8% for temperature-based Rs, 16.1%, 21.5% and 24.5% for sunshine-based Rd, and 16.8%, 22.0% and 23.3% for temperature-based Rd. The results demonstrate the importance of appropriate selection of air pollution inputs to improve the accuracy of Rs and Rd prediction in air-polluted regions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Renewable and Sustainable Energy Reviews - Volume 94, October 2018, Pages 732-747
نویسندگان
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