کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5451353 1513077 2017 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Quantifying rooftop photovoltaic solar energy potential: A machine learning approach
ترجمه فارسی عنوان
پتانسیل خورشیدی انرژی خورشیدی فتوولتائیک را کمینه می کند: روش یادگیری ماشین
کلمات کلیدی
فراگیری ماشین، رگرسیون بردار پشتیبانی، فتوولتائیک خورشیدی، پتانسیل خورشیدی در مقیاس بزرگ، سیستم های اطلاعات جغرافیایی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی


- Rooftop photovoltaic solar energy potential has been quantified in Switzerland.
- Geographic Information Systems (GIS) tools were used for data processing.
- Support Vector Regression was used to predict solar and urban characteristics.
- Annual potential PV production in Switzerland is estimated at 17.86 TW h.

The need for reduction in CO2 emissions to mitigate global warming has resulted in increasing use of renewable energy sources. In urban areas, solar photovoltaic (PV) deployment on existing rooftops is one of the most viable sustainable energy resources. The present study uses a combination of support vector machines (SVMs) and geographic information systems (GIS) to estimate the rooftop solar PV potential for the urban areas at the commune level (the smallest administrative division) in Switzerland. The rooftop solar PV potential for a total 1901 out of 2477 communes in Switzerland is estimated. Following a 6-fold cross validation, the root-mean-square error (also normalized) is used to estimate the accuracy of the different SVM models. The results show that, on average, 81% of the total ground floor area of each building corresponds to the available roof area for the PV installation. Also considering the total available roof area for PV installation, that is, 328 km2 and roof orientations within ±90° of due south, the annual potential PV electricity production for the urban areas in Switzerland is estimated at 17.86 TW h (assumed 17% efficiency and 80% performance ratio). This amount corresponds to 28% of Switzerland's electricity consumption in 2015.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Solar Energy - Volume 141, 1 January 2017, Pages 278-296
نویسندگان
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