کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1732357 | 1521462 | 2015 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks
ترجمه فارسی عنوان
مدلسازی مصرف انرژی و میزان گازهای گلخانه ای مرتبط با گازهای گلخانه ای در اروپا با استفاده از شبکه های عصبی رگرسیون عمومی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
چکیده انگلیسی
This paper presents a new approach for the estimation of energy-related GHG (greenhouse gas) emissions at the national level that combines the simplicity of the concept of GHG intensity and the generalization capabilities of ANNs (artificial neural networks). The main objectives of this work includes the determination of the accuracy of a GRNN (general regression neural network) model applied for the prediction of EC (energy consumption) and GHG intensity of energy consumption, utilizing general country statistics as inputs, as well as analysis of the accuracy of energy-related GHG emissions obtained by multiplying the two aforementioned outputs. The models were developed using historical data from the period 2004-2012, for a set of 26Â European countries (EU Members). The obtained results demonstrate that the GRNN GHG intensity model provides a more accurate prediction, with the MAPE (mean absolute percentage error) of 4.5%, than tested MLR (multiple linear regression) and second-order and third-order non-linear MPR (multiple polynomial regression) models. Also, the GRNN EC model has high accuracy (MAPEÂ =Â 3.6%), and therefore both GRNN models and the proposed approach can be considered as suitable for the calculation of GHG emissions. The energy-related predicted GHG emissions were very similar to the actual GHG emissions of EU Members (MAPEÂ =Â 6.4%).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 84, 1 May 2015, Pages 816-824
Journal: Energy - Volume 84, 1 May 2015, Pages 816-824
نویسندگان
Davor AntanasijeviÄ, Viktor Pocajt, Mirjana RistiÄ, Aleksandra PeriÄ-GrujiÄ,