Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks

Energy

Volumn 84, Issue , 2015, Pages 816-824

  Antanasijević, Davor ^a   Pocajt, Viktor ^a   Ristić, Mirjana ^a   Perić Grujić, Aleksandra ^a  

^a UNIVERSITY OF BELGRADE   (Serbia)

Author keywords

Artificial neural networks; GRNN (general regression neural network); Multiple linear regression; Multiple polynomial regression

Indexed keywords

ENERGY UTILIZATION; GAS EMISSIONS; GREENHOUSE GASES; LINEAR REGRESSION; NEURAL NETWORKS;

ACCURATE PREDICTION; EUROPEAN COUNTRIES; GENERAL REGRESSION NEURAL NETWORK; GENERALIZATION CAPABILITY; MEAN ABSOLUTE PERCENTAGE ERROR; MODEL OF ENERGY CONSUMPTION; MULTIPLE LINEAR REGRESSIONS; MULTIPLE POLYNOMIAL REGRESSIONS;

POLYNOMIAL REGRESSION;

ACCURACY ASSESSMENT; EMISSION CONTROL; ENERGY USE; ENVIRONMENTAL POLICY; GREENHOUSE GAS; NATIONAL PLANNING; NONLINEARITY;

EUROPE;

EID: 84928437188     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2015.03.060     Document Type: Article

References (48)

1. Xu X., Zhao T., Liu N., Kang J. Changes of energy-related GHG emissions in China: an empirical analysis from sectoral perspective. Appl Energy 2014, 132:298-307.
2. Bhattacharyya S.C., Matsumura W. Changes in the GHG emission intensity in EU-15: lessons from a decomposition analysis. Energy 2010, 35:3315-3322.
3. United Nations Framework Convention on Climate Change Doha amendment to the kyoto protocol 2012, Qatar, Doha. http://unfccc.int/kyoto_protocol/doha_amendment/items/7362.php.
4. Hercog T., Baumert K.A., Pershing J. Target: intensity. An analysis of greenhouse gas intensity 2006, World Resources Institute, Washington, USA.
5. U.S. Department of Energy (US DOE), Energy Information Administration (EIA). International energy Outlook. Wash D.C.: 2006.
6. Winiwarter W., Rypdal K. Assessing the uncertainty associated with national greenhouse gas emission inventories: a case study for Austria. Atmos Environ 2001, 35:5425-5440.
7. Akimoto K., Sano F., Homma T., Oda J., Nagashima M., Kii M. Estimates of GHG emission reduction potential by country, sector, and cost. Energy Policy 2010, 38:3384-3393.
8. Chicco G., Mancarella P. Assessment of the greenhouse gas emissions from cogeneration and trigeneration systems. Part I: models and indicators. Energy 2008, 33:410-417.
9. Couth R., Trois C., Vaughan-Jones S. Modelling of greenhouse gas emissions from municipal solid waste disposal in Africa. Int J Greenh Gas Control 2011, 5:1443-1453.
10. Hediger W. Modeling GHG emissions and carbon sequestration in Swiss agriculture: an integrated economic approach. Int Congr Ser 2006, 1293:86-95.
11. Rentziou A., Gkritza K., Souleyrette R.R. VMT, energy consumption, and GHG emissions forecasting for passenger transportation. Transp Res Part A Policy Pract 2012, 46:487-500.
12. Syri S., Lehtila A., Ekholm T., Savolainen I., Holttinen H., Peltola E. Global energy and emissions scenarios for effective climate change mitigation-deterministic and stochastic scenarios with the TIAM model. Int J Greenh Gas Control 2008, 2:274-285.
13. Villalba G., Gemechu E.D. Estimating GHG emissions of marine ports-the case of Barcelona. Energy Policy 2011, 39:1363-1368.
14. Mancarella P., Chicco G. Assessment of the greenhouse gas emissions from cogeneration and trigeneration systems. Part II: analysis techniques and application cases. Energy 2008, 33:418-430.
15. UNFCCC. Response measures models - PRIMES, [accessed 17.11.2014.]. https://unfccc.int/adaptation/adverse_effects_and_response_measures_art_48/items/5149.php.
16. Institute of Communication and Computer Systems of the National Technical University of Athens (ICCS/NTUA). The PRIMES model, [accessed 17.11.2014.]. http://www.e3mlab.ntua.gr/e3mlab/index.php?option=com_content%26view=category%26id=35:primes%26Itemid=80%26layout=default%26lang=en.
17. Aydin G. Modeling of energy consumption based on economic and demographic factors: the case of Turkey with projections. Renew Sustain Energy Rev 2014, 35:382-389.
18. Aydinalp-Koksal M., Ugursal V.I. Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector. Appl Energy 2008, 85:271-296.
19. Swan L.G., Ugursal V.I. Modeling of end-use energy consumption in the residential sector: a review of modeling techniques. Renew Sustain Energy Rev 2009, 13:1819-1835.
20. Taghavifar H., Mardani A. Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using artificial neural network. JClean Prod 2015, 87:159-167.
21. Nabavi-Pelesaraei A., Abdi R., Rafiee S. Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems. JSaudi Soc Agric Sci 2014, 10.1016/j.jssas.2014.05.001.
22. Yousefi M., Khoramivafa M., Mondani F. Integrated evaluation of energy use, greenhouse gas emissions and global warming potential for sugar beet (Beta vulgaris) agroecosystems in Iran. Atmos Environ 2014, 92:501-505.
23. Yousefi M., Damghani A.M., Khoramivafa M. Energy consumption, greenhouse gas emissions and assessment of sustainability index in corn agroecosystems of Iran. Sci Total Environ 2014, 493:330-335.
24. Khoshnevisan B., Rafiee S., Omid M., Yousefi M., Movahedi M. Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy 2013, 52:333-338.
25. Bevilacqua V., Intini F., Kühtz S. Amodel of artificial neural network for the analysis of climate change. Proceeding of the 29th annual International Sympsium on forecasting, Nice, France 2008, [accessed 09.03.2015.]. http://forecasters.org/pdfs/isf/ISF2008_Proceedings.pdf.
26. Radojević D., Pocajt V., Popović I., Perić-Grujić A., Ristić M. Forecasting of greenhouse gas emissions in Serbia using artificial neural networks. Energy Sources Part A 2013, 35:733-740.
27. Antanasijević D.Z., Ristić M.D., Perić-Grujić A.A., Pocajt V.V. Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis. Int J Greenh Gas Control 2014, 20:244-253.
28. Lee W.-Y., House J.M., Kyong N.-H. Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks. Appl Energy 2004, 77:153-170.
29. Eurostat. GHG emissions, [accessed 7.11.2014.]. http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_air_gge%26lang=en.
30. Eurostat. Gross inland energy consumption, [accessed 7.11.2014.]. http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_100a%26lang=en.
31. Eurostat. Share of renewable energy in gross final energy consumption, [accessed 7.11.2014.]. http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table%26init=1%26plugin=1%26language=en%26pcode=t2020_31.
32. Eurostat. Combined heat and power generation, [accessed 7.11.2014.]. http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table%26init=1%26plugin=1%26language=en%26pcode=tsdcc350.
33. Eurostat. Share of renewable energy in fuel consumption of transport, [accessed 7.11.2014.]. http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table%26init=1%26plugin=1%26language=en%26pcode=tsdcc340.
34. Eurostat. Implicit tax rate on energy, [accessed 7.11.2014.]. http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table%26init=1%26plugin=1%26language=en%26pcode=tsdcc360.
35. Eurostat. Database, [accessed 7.11.2014.]. http://ec.europa.eu/eurostat/data/database.
36. The World Bank. World development indicators, [accessed 7.11.2014.]. http://data.worldbank.org/data-catalog/world-development-indicators.
37. Ben-Nakhi A.E., Mahmoud M a Energy conservation in buildings through efficient A/C control using neural networks. Appl Energy 2002, 73:5-23.
38. Heddam S. Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA. Environ Technol 2014, 35:1650-1657.
39. Specht D.F. Ageneral regression neural network. IEEE Trans Neural Netw 1991, 2:568-576.
40. Antanasijević D.Z., Pocajt V.V., Povrenović D.S., Ristić M.D., Perić-Grujić A.A. PM 10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 2013, 443:511-519.
41. Kalogirou S.A. Artificial intelligence for the modeling and control of combustion processes: a review. Prog Energy Combust Sci 2003, 29:515-566.
42. Kim S., Kim H.S. Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. JHydrol 2008, 351:299-317.
43. Chen Y.-H., Chang F.-J. Evolutionary artificial neural networks for hydrological systems forecasting. JHydrol 2009, 367:125-137.
44. Willmott C.J., Robeson S.M., Matsuura K. Arefined index of model performance. Int J Climatol 2012, 32:2088-2094.
45. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models: Part 1. A discussion of principles. JHydrol 1970, 10:282-290.
46. 2 emissions, energy consumption and economic growth in China using an improved grey model. Energy 2012, 40:400-409.
47. Moriasi D.N., Arnold J.G., Liew MW Van, Bingner R.L., Harmel R.D., Veith T.L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 2007, 50:885-900.
48. Schöpp W., Klimont Z., Suutari R., Cofala J. Uncertainty analysis of emission estimates in the RAINS integrated assessment model. Environ Sci Policy 2005, 8:601-613.