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[J]. 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. doi: 10.1016/j.energy.2018.11.040 |
[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. doi: 10.1016/j.ecoenv.2017.11.060 |
[3] | Kamali M, Kamali AR (2018) Preparation of borax pentahydrate from effluents of iron nanoparticles synthesis process. AIMS Energy 6: 1067–1073. doi: 10.3934/energy.2018.6.1067 |
[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. doi: 10.1016/j.triboint.2017.05.010 |
[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. doi: 10.1016/j.energy.2013.08.018 |
[7] | Ediger VŞ, Akar S (2007) ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy 35: 1701–1708. doi: 10.1016/j.enpol.2006.05.009 |
[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. doi: 10.1016/j.energy.2016.02.001 |
[9] | Zhang PG (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50: 159–175. doi: 10.1016/S0925-2312(01)00702-0 |
[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. doi: 10.1016/j.ijepes.2016.03.012 |
[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. doi: 10.1016/j.energy.2017.12.049 |
[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. doi: 10.1016/j.energy.2016.10.068 |
[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. doi: 10.1016/j.rser.2016.01.086 |
[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. doi: 10.1016/j.egypro.2017.03.795 |
[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. doi: 10.1080/17512549.2017.1287127 |
[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. doi: 10.1016/j.solener.2019.01.004 |
[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. doi: 10.1016/j.apenergy.2018.10.093 |
[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. doi: 10.1016/j.jenvp.2015.11.001 |
[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. doi: 10.1021/acs.estlett.6b00324 |
[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. doi: 10.1016/j.egypro.2013.06.701 |
[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. |