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Energy Efficiency

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01.04.2016 | Original Article

Selection of the best ARMAX model for forecasting energy demand: case study of the residential and commercial sectors in Iran

verfasst von: Hamed Shakouri G., Aliyeh Kazemi

Erschienen in: Energy Efficiency | Ausgabe 2/2016

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Abstract

The main purpose of the present study is to develop a simple yet proper top-down model for forecasting the energy demand of the residential and commercial sectors in Iran. This model can be used as a tool of scenario analysis to predict the emerging energy demand in future. The proposed model would be systematically developed and selected based on various quantified exogenous variables. For this purpose, a certain model out of a collection of 41,472 parallel models with different inputs and dynamics is chosen as the most appropriate model. According to the logical conjunctive relationships between the variables, the structure of all competing models is established to log-linear. Different possible combinations of various measures for the exogenous variables generate parallel models. Then, an automated fuzzy decision-making (FDM) process determines the best model. Finally, defining several scenarios, the energy demand of the residential and commercial sectors in Iran for the period of 2013 to 2021 is forecasted. The results showed that despite of de-subsidization, which is included by a dummy variable, the energy demand will grow by an average rate of about 3 % annually.

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Since 1980 till present, the average 10-year share of industrial sector from total value added of the country has been increased by almost 10 % each decade.

Kwiatkowski–Phillips–Schmidt–Shin test

Aras, H., & Aras, N. (2004). Forecasting residential natural gas demand. Energy Sources, 26, 463–472.CrossRef

Bilgili, M., Sahin, B., Yasar, A., & Simsek, E. (2012). Electric energy demands of Turkey in residential and industrial sectors. Renewable and Sustainable Energy Reviews, 16, 404–414.CrossRef

Canyurt, O. E., Ozturk, K. H., Hepbasli, A., & Utlu, Z. (2005). Estimating the Turkish residential–commercial energy output based on genetic algorithm (GA) approaches. Energy Policy, 33(8), 1011–1019.CrossRef

Catalina, T., Virgone, J., & Blanco, E. (2008). Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and Buildings, 40(10), 1825–1832.CrossRef

Catalina, T., Iordache, V., & Caracaleanu, B. (2013). Multiple regression model for fast prediction of the heating energy demand. Energy and Buildings, 57, 302–312.CrossRef

Cuddihy, J., Kennedy, C., & Byer, P. (2005). Energy use in Canada: environmental impacts and opportunities in relationship to infrastructure systems. Canadian Journal of Civil Engineering, 32(1), 1–15. doi:10.1139/l04-100.CrossRef

Dilaver, Z., & Hunt, L. C. (2011). Modelling and forecasting Turkish residential electricity demand. Energy Policy, 39, 3117–3127.CrossRef

Donatos, G. S., & Mergos, G. J. (1991). Residential demand for electricity: the case of Greece. Energy Economics, 41–47.

Energy Balance Sheet 2011 (2012). Energy Planning Department, Deputy of Electricity and Energy Affairs, Ministry of Energy, Islamic Republic of Iran.

Forouzanfar, M., Doustmohammadi, A., Menhaj, M. B., Hasanzadeh, S., & Shakouri G., H. (2010). Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran. Applied Energy, 87, 268–274.CrossRef

Grasa, A. A. (1989). Econometric model selection: A new approach. Norwell: Kluwer.CrossRef

Halicioglu, F. (2007). Residential electricity demand dynamics in Turkey. Energy Economics, 29, 199–210.CrossRef

Hsing, Y. (1994). Estimation of residential demand for electricity with the cross-sectionally correlated and time-wise autoregressive model. Resource and Energy Economics, 16, 255–263.CrossRef

Hydrocarbon Balance 2010, (2011). Institute for International Energy Studies (IIES), Ministry of Petroleum, Islamic Republic of Iran.

Jung, T. Y. (1993). Ordered logit model for residential electricity demand in Korea. Energy Economics, 205–209.

Kavgica, M., Mavrogiannia, A., Mumovica, D., Summerfieldb, A., Stevanovicc, Z., & Djurovic-Petrovicd, M. (2010). A review of bottom-up building stock models for energy consumption in the residential sector. Building and Environment, 45(7), 1683–1697.CrossRef

Kazemi, A., Shakouri G., H., Menhaj, M. B., Mehregan, M. R., Neshat, N. (2010). A multi-level artificial neural network for residential and commercial energy demand forecast: Iran case study. International Conference on Management Technology and Applications, 24–28.

Kheirkhah, A., Azadeh, A., Saberi, M., Azaron, A., & Shakouri, H. (2013). Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis. Computers & Industrial Engineering, 64(1), 425–441.CrossRef

Kialashaki, A., & Reisel, J. R. (2013). Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks. Applied Energy, 108, 271–280.CrossRef

Lam, J. C. (1998). Climatic and economic influences on residential electricity consumption. Energy Conversion and Management, 39(7), 623–629.CrossRef

Ljung, L. (1987). System identification: Theory for the user. Prentic Hall.

Moradi, M. A., Shakouri G., H., Aboutaleb, A. M. (2013). Developing the electricity demand model for Iran’s residential sector; based on LEAP, 28th International Power Systems Conference, Tehran, Iran.

Nakajima, T., & Hamori, S. (2010). Change in consumer sensitivity to electricity prices in response to retail deregulation: A panel empirical analysis of the residential demand for electricity in the United States. Energy Policy, 38, 2470–2476.CrossRef

Narayan, P. K., Smyth, R., & Prasad, A. (2007). Electricity consumption in G7 countries: a panel cointegration analysis of residential demand elasticities. Energy Policy, 35, 4485–4494.CrossRef

Overcash, M., & Bawaneh, K. (2013). Estimating nonprocess energy from building energy consumption. Energy Efficiency, 6, 21–33.CrossRef

Ozturk, H. K., Canyurt, O. E., Hepbasli, A., & Utlu, Z. (2004). Residential–commercial energy input estimation based on genetic algorithm (GA) approached: an application of Turkey. Energy and Buildings, 36, 175–183.CrossRef

Pourazarm, E., & Cooray, A. (2013). Estimating and forecasting residential electricity demand in Iran. Economic Modelling, 35, 546–558.CrossRef

Poyer, D. A., & Williams, M. (1993). Residential energy demand: additional empirical evidence by minority household type. Energy Economics, 93–100.

Rapanos, V. T., & Polemis, M. L. (2006). The structure of residential energy demand in Greece. Energy Policy, 34, 3137–3143.CrossRef

Rodger, J. A. (2014). A fuzzy nearest neighbor neural network statistical model for predicting, demand for natural gas and energy cost savings in public buildings. Expert Systems with Applications, 41, 1813–1829.CrossRef

Ruijven, B., Vries, B., Vuuren, D. P., & Sluijs, J. P. (2010). A global model for residential energy use: uncertainty in calibration to regional data. Energy, 35, 269–282.CrossRef

Saad, S. (2009). Electricity demand for South Korean residential sector. Energy Policy, 37, 5469–5474.CrossRef

Shahmoradi, A., & Shakouri G., H. (2010). Investigation on the impact of an energy desubsidization shock on the general price index via a nonlinear inflation model: case of Iran. Iranian Economic Review, 15(27), 33–51.

Shakouri G., H., & Menhaj, M. B. (2008). A systematic fuzzy decision-making process to choose the best model among a set of competing models. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans , 38(5), 1118–1128.CrossRef

Shakouri G., H., Rastad, M., & Nazarzadeh, J. (2006). A hybrid nonlinear model for the annual maximum simultaneous electric power demand. IEEE Transactions on Power Systems, 21(3), 1069–1078.CrossRef

Shakouri G., H. (2000). Modeling and identification of Iranian macroeconomic dynamic system (systems approach), Ph.D. Dissertation, Amirkabir University of Technology.

Soldo, B., Potocnik, P., Simunovic, G. Saric, T., Govekar, E. (2013). Improving the residential natural gas consumption forecasting models by using solar radiation. Energy and Buildings, In Press, Accepted Manuscript, Available online 20 Nov 2013.

Statistics; Central Bank of Iran (CBI) (2012). Available at: http://www.cbi.ir/section/1372.aspx. Accessed 20 Aug 2014.

Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: a review of modeling techniques. Renewable and Sustainable Energy Reviews, 13, 1819–1835.CrossRef

Taspınar, F., Celebi, N., & Tutkun, N. (2013). Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods. Energy and Buildings, 56, 23–31.CrossRef

Yoo, S. H., Zhu, K., Lee, J. S., & Kwak, S. J. (2007). Estimation of residential electricity demand function in Seoul by correction for sample selection bias. Energy Policy, 35, 5702–5707.CrossRef

Yu, S., Zhu, K., & Zhang, X. (2012a). Energy demand projection of China using a path-coefficient analysis and PSO-GA approach. Energy Conversion and Management, 53(1), 142–153.CrossRef

Yu, S., Wei, Y. M., & Wang, K. (2012b). Prediction of China’s coal production—environmental pollution based on a hybrid genetic algorithm-system dynamics model. Energy Policy, 42, 521–529.CrossRef

Zhang, Q. (2004). Residential energy consumption in China and its comparison with Japan, Canada, and USA. Energy and Buildings, 36, 1217–1225.CrossRef

Zhaoa, X., Lia, N., & Ma, C. (2012). Residential energy consumption in urban China: a decomposition analysis. Energy Policy, 41, 644–653.CrossRef

Ziramba, E. (2008). The demand for residential electricity in South Africa. Energy Policy, 36, 3460–3466.CrossRef

Titel: Selection of the best ARMAX model for forecasting energy demand: case study of the residential and commercial sectors in Iran
verfasst von: Hamed Shakouri G.
Aliyeh Kazemi
Publikationsdatum: 01.04.2016
Verlag: Springer Netherlands
Erschienen in: Energy Efficiency / Ausgabe 2/2016
Print ISSN: 1570-646X
Elektronische ISSN: 1570-6478
DOI: https://doi.org/10.1007/s12053-015-9368-9

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