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An Auto Regressive Integrated Moving Average (ARIMA) Model for prediction of energy consumption by household sector in Euro area

1 Department of Economics, Management, Industrial Engineering and Tourism, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal
2 Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal
3 Department of Biology, University of Aveiro, 3810-193 Aveiro, Portugal
4 Department of Environment and Planning, Center for Environmental and Marine Studies, CESAM, University of Aveiro, 3810-193 Aveiro, Portugal
5 Department of Materials and Ceramics Engineering, Aveiro Institute of Materials, CICECO, University of Aveiro, 3810-193 Aveiro, Portugal

Special Issues: Industrial symbiosis: waste management practices within industries for sustainable environment

Accurate estimation of the energy need and consumption is considered as one of the most important basis of the economy worldwide. It is also of high importance to mitigate the adverse effects of the release of CO2 (e.g., climate change) from conventional energy sources by using renewable energies, as recommended by European commission. Thus, in this study a forecast regarding the residential energy consumption of the household sector in countries belonging to the Euro area was executed. To proceed with this prediction, time related data from 1990 till 2015 along with Auto Regressive Integrated Moving Average (ARIMA) model were applied. ARIMA model was considered due to possessing the ability of providing accurate results while being able to receive stationary and non-stationary data. The obtained results from the analysis clarified that ARIMA (0,1,1) model is the most accurate model to undertake such prediction as the amount of RMSE achieved was 0.097. This comparison was accomplished by considering the ARIMA (0,1,0) and ARIMA (1,1,2) models as their amounts regarding RMSE were respectively 0.1068149 and 0.0975575. The results indicate that the amount of the energy predicted to be consumed in household sector in EU area is estimated to be 186244 toe (tonne of oil equivalent) which shows a drop in the energy consumption in Euro area probably due to the increase in the energy efficiency especially in recent years. 
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Keywords household energy consumption; R program; ARIMA; RMSE; forecasting

Citation: Akram Jahanshahi, Dina Jahanianfard, Amid Mostafaie, Mohammadreza Kamali. An Auto Regressive Integrated Moving Average (ARIMA) Model for prediction of energy consumption by household sector in Euro area. AIMS Energy, 2019, 7(2): 151-164. doi: 10.3934/energy.2019.2.151


  • 1. Jahanshahi A, Kamali M, Khalaj M, et al. (2019) Delphi-based prioritization of economic criteria for development of wave and tidal energy technologies. Energy 167: 819–827.    
  • 2. Khalaj M, Kamali M, Khodaparast Z, et al. (2018) Copper-based nanomaterials for environmental decontamination-An overview on technical and toxicological aspects. Ecotoxicol Environ Saf 148: 813–824.    
  • 3. Kamali M, Kamali AR (2018) Preparation of borax pentahydrate from effluents of iron nanoparticles synthesis process. AIMS Energy 6: 1067–1073.    
  • 4. Holmberg K, Kivikytö-Reponen P, Härkisaari P, et al. (2017) Global energy consumption due to friction and wear in the mining industry. Tribol Int 115: 116–139.    
  • 5. Li S, Li R (2017) Comparison of forecasting energy consumption in Shandong, China using the ARIMA model, GM model, and ARIMA-GM model. Sustainability 9: 1–19.
  • 6. Santiago I, López-Rodríguez MA, Gil-de-Castro A, et al. (2013) Energy consumption of audiovisual devices in the residential sector: Economic impact of harmonic losses. Energy 60: 292–301.    
  • 7. Ediger VŞ, Akar S (2007) ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35: 1701–1708.    
  • 8. Yuan C, Liu S, Fang Z (2016) Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1,1) model. Energy 100: 384–390.    
  • 9. Zhang PG (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50: 159–175.    
  • 10. Rahman A, Ahmar AS (2017) Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holter-Winters models. AIP Conf Proc 1885.
  • 11. Barak S, Sadegh SS (2016) Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm. Int J Electr Power Energy Syst 82: 92–104.    
  • 12. 12 de Oliveira EM, Cyrino Oliveira FL (2018) Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy 144: 776–788.    
  • 13. Nichiforov C, Stamatescu I, Fagarasan I, et al. (2017) Energy consumption forecasting using ARIMA and neural network models. Proc. - 2017 5th Int Symp Electr Electron Eng ISEEE: 1–4.
  • 14. Sen P, Roy M, Pal P (2016) Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization. Energy 116: 1031–1038.    
  • 15. Kaivo-Oja J, Vehmas J, Luukkanen J (2016) Trend analysis of energy and climate policy environment: Comparative electricity production and consumption benchmark analyses of China, Euro area, European Union, and United States. Renew Sustain Energy Rev 60: 464–474.    
  • 16. PORDATA - Statistics, charts and indicators on Municipalities, Portugal and Europe. Available from: https://www.pordata.pt/en/Home.
  • 17. Shumway RH, Stoffer DS (2017) Time Series Analysis and Its Applications with examples. 4 Eds., Springer, 417–437.
  • 18. Brockwell PJ, Davis RA (2016) Introduction to Time Series and Forecasting. Springer.
  • 19. Box GEP, Jenkins GM, Reinsel GC, et al. (2015) Time series analysis: Forecasting and control. 5 Eds., San Francisco: Holden-Day.
  • 20. Haiges R, Wang YD, Ghoshray A, et al. (2017) Forecasting electricity generation capacity in Malaysia: An Auto Regressive Integrated Moving Average Approach. Energy Procedia 105: 3471–3478.    
  • 21. Burroughs S (2018) Improving office building energy-efficiency ratings using a smart-engineering–computer-simulation approach: an Australian case study. Adv Build Energy Res 12: 217–234.    
  • 22. Delgado Marín JP, Vera García F, García Cascales JR (2019) Use of a predictive control to improve the energy efficiency in indoor swimming pools using solar thermal energy. Sol Energy 179: 380–390.    
  • 23. Hossieny N, Shrestha SS, Owusu OA, et al. (2019) Improving the energy efficiency of a refrigerator-freezer through the use of a novel cabinet/door liner based on polylactide biopolymer. Appl Energy 235: 1–9.    
  • 24. Fornara F, Pattitoni P, Mura M, et al. (2016) Predicting intention to improve household energy efficiency: The role of value-belief-norm theory, normative and informational influence, and specific attitude. J Environ Psychol 45: 1–10.    
  • 25. Li Q, Jiang J, Qi J, et al. (2016) Improving the energy efficiency of stoves to reduce pollutant emissions from household solid fuel combustion in China. Environ Sci Technol Lett 3: 369–374.    
  • 26. European Environment Agency, 'Household energy consumption', 2018. Available from: https://www.eea.europa.eu/airs/2018/resource-efficiency-and-low-carbon-economy/household-energy-consumption.
  • 27. IPCC, 2011, IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation, O. Edenhofer, R. Pichs-Madruga, Sokona Y, Seyboth K, Matschoss P, Kadner S, Zwickel T, Eickemeier P, Hansen G, Schlömer S, von Stechow C, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  • 28. Odenberger M, Kjärstad J, Johnsson F (2013) Prospects for CCS in the EU energy roadmap to 2050. Energy Procedia 37: 7573–7581.    
  • 29. European Comission, Clean energy for all Europeans, Available from: https://ec.europa.eu/energy/en/topics/energy-strategy-and-energy-union/clean-energy-all europeans.
  • 30. Transforming our world: the 2030 Agenda for Sustainable Development: Sustainable Development Knowledge Platform.
  • 31. Jorant C (2011), The implications of Fukushima the European perspective. Bull At Sci 67: 14–17.
  • 32. Kádár P (2014) Pros and cons of the renewable energy application. Acta Polytech Hungarica 11: 211–224.
  • 33. European Comission (2011) 'Energy roadmap 2050', Available from: https://ec.europa.eu/energy/sites/ener/files/documents/2012_energy_roadmap_2050_en_0.pdf.


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