Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks
Само за регистроване кориснике
2015
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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 26 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 (MAPE = 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 (MAPE = 6.4%).
Кључне речи:
Artificial neural networks / GRNN (general regression neural network) / Multiple linear regression / Multiple polynomial regressionИзвор:
Energy, 2015, 84, 816-824Издавач:
- Pergamon-Elsevier Science Ltd, Oxford
Финансирање / пројекти:
- Развој и примена метода и материјала за мониторинг нових загађујућих и токсичних органских материја и тешких метала (RS-MESTD-Basic Research (BR or ON)-172007)
DOI: 10.1016/j.energy.2015.03.060
ISSN: 0360-5442
WoS: 000355035900077
Scopus: 2-s2.0-84928437188
Институција/група
Tehnološko-metalurški fakultetTY - JOUR AU - Antanasijević, Davor AU - Pocajt, Viktor AU - Ristić, Mirjana AU - Perić-Grujić, Aleksandra PY - 2015 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/2987 AB - 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 26 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 (MAPE = 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 (MAPE = 6.4%). PB - Pergamon-Elsevier Science Ltd, Oxford T2 - Energy T1 - Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks EP - 824 SP - 816 VL - 84 DO - 10.1016/j.energy.2015.03.060 ER -
@article{ author = "Antanasijević, Davor and Pocajt, Viktor and Ristić, Mirjana and Perić-Grujić, Aleksandra", year = "2015", abstract = "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 26 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 (MAPE = 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 (MAPE = 6.4%).", publisher = "Pergamon-Elsevier Science Ltd, Oxford", journal = "Energy", title = "Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks", pages = "824-816", volume = "84", doi = "10.1016/j.energy.2015.03.060" }
Antanasijević, D., Pocajt, V., Ristić, M.,& Perić-Grujić, A.. (2015). Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. in Energy Pergamon-Elsevier Science Ltd, Oxford., 84, 816-824. https://doi.org/10.1016/j.energy.2015.03.060
Antanasijević D, Pocajt V, Ristić M, Perić-Grujić A. Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. in Energy. 2015;84:816-824. doi:10.1016/j.energy.2015.03.060 .
Antanasijević, Davor, Pocajt, Viktor, Ristić, Mirjana, Perić-Grujić, Aleksandra, "Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks" in Energy, 84 (2015):816-824, https://doi.org/10.1016/j.energy.2015.03.060 . .