How to cite this paper
Neshat, N., Hadian, H & Behzad, M. (2018). Nonlinear ARIMAX model for long –term sectoral demand forecasting.Management Science Letters , 8(6), 581-592.
Refrences
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Amarawickrama, H. A., & Hunt, L. C. (2008). Electricity demand for Sri Lanka: a time series analy-sis. Energy, 33(5), 724-739.
Awan, S. M., Khan, Z. A., Aslam, M., Mahmood, W., & Ahsan, A. (2012, May). Application of NARX based FFNN, SVR and ANN Fitting models for long term industrial load forecasting and their comparison. In Industrial Electronics (ISIE), 2012 IEEE International Symposium on (pp. 803-807). IEEE.
Azadeh, A., Neshat, N., Rafiee, K., & Zohrevand, A. M. (2012). An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: the case of Iran. Transportation Planning and Technology, 35(2), 221-240.
Barakat, E. E., & Al-Rashed, S. A. (1992). Long range peak demand forecasting under conditions of high growth. IEEE Transactions on Power Systems, 7(4), 1483-1486.
Battistelli, C., Carpinelli, G., Mottola, F., Proto, D., & Zoppoli, D. (2011). Distribution networks with linear and nonlinear loads: cost-based method for planning dispersed generation and capacitor units under uncertainty. Journal of Energy Engineering, 137(1), 44-58.
Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421.
Camara, A., Feixing, W., & Xiuqin, L. (2016). Energy consumption forecasting using seasonal ARIMA with artificial neural networks models. International Journal of Business and Management, 11(5), 231.
Connor, J. D. (1987). The demand for electricity in western US irrigated agriculture: a dual cost func-tion analysis.
Daim, T. U., & Oliver, T. (2008). Implementing technology roadmap process in the energy services sec-tor: A case study of a government agency. Technological Forecasting and Social Change, 75(5), 687-720.
De Castro, J. B., & de Ávila Montini, A. (2010). FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL. Future Studies Research Journal: Trends and Strategies, 2(2), 3-16.
Dilaver, Z., & Hunt, L. C. (2011). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426-436.
Durán, M. J., Cros, D., & Riquelme, J. (2007). Short-term wind power forecast based on ARX mod-els. Journal of Energy Engineering, 133(3), 172-180.
Elkarmi, F. (2008). Load research as a tool in electric power system planning, operation, and control—The case of Jordan. Energy Policy, 36(5), 1757-1763.
Gabriel, S. A., Sahakij, P., & Balakrishnan, S. (2004). Optimal retailer load estimates using stochastic dynamic programming. Journal of Energy Engineering, 130(1), 1-17.
Ghanbari, A., Kazemi, S. M., Mehmanpazir, F., & Nakhostin, M. M. (2013). A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowledge-Based Systems, 39, 194-206.
Glasnovic, Z., & Margeta, J. (2010). Sustainable electric power system: Is it possible? Case study: Croa-tia. Journal of Energy Engineering, 136(4), 103-113.
Haas, R., & Schipper, L. (1998). Residential energy demand in OECD-countries and the role of irre-versible efficiency improvements. Energy economics, 20(4), 421-442.
Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009). Electric load forecasting methods: Tools for decision making. European journal of operational research, 199(3), 902-907.
Kani, S. A. P., & Ershad, N. F. (2007, October). Annual electricity demand prediction for Iranian agri-culture sector using ANN and PSO. In Electrical Power Conference, 2007. EPC 2007. IEEE Cana-da (pp. 446-451). IEEE.
Kazemi, A., Shakouri, H. G., Menhaj, M. B., Mehregan, M. R., & Neshat, N. (2011). A hierarchical fuzzy linear regression model for forecasting agriculture energy demand: A case study of Iran. In 2011 3rd International Conference on Information and Financial Engineering (Vol. 12, pp. 19-24).
Kucukali, S., & Baris, K. (2010). Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach. Energy Policy, 38(5), 2438-2445.
Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137, 47-56.
Magnano, L., & Boland, J. W. (2007). Generation of synthetic sequences of electricity demand: Appli-cation in South Australia. Energy, 32(11), 2230-2243.
Neto, A. H., & Fiorelli, F. A. S. (2008). Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and buildings, 40(12), 2169-2176.
Newsham, G. R., & Birt, B. J. (2010, November). Building-level occupancy data to improve ARIMA-based electricity use forecasts. In Proceedings of the 2nd ACM workshop on embedded sensing sys-tems for energy-efficiency in building (pp. 13-18). ACM.
Nocedal, J., & Wright, S. (2006). Numerical optimization, series in operations research and financial en-gineering. Springer, New York, USA, 2006.
Nwobi-Okoye, C. C., & Igboanugo, A. C. (2015). Performance appraisal of gas based electric power generation system using transfer function modelling. Ain Shams Engineering Journal, 6(2), 541-551.
Pao, H. T. (2009). Forecasting energy consumption in Taiwan using hybrid nonlinear mod-els. Energy, 34(10), 1438-1446.
Porkar, S., Poure, P., Abbaspour-Tehrani-fard, A., & Saadate, S. (2010). A novel optimal distribution system planning framework implementing distributed generation in a deregulated electricity mar-ket. Electric Power Systems Research, 80(7), 828-837.
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.
Sarduy, J. R. G., Di Santo, K. G., & Saidel, M. A. (2016). Linear and non-linear methods for prediction of peak load at University of São Paulo. Measurement, 78, 187-201.
Sharma, D. P., Nair, P. C., & Balasubramanian, R. (2002). Demand for commercial energy in the state of Kerala, India: an econometric analysis with medium-range projections. Energy policy, 30(9), 781-791.
Sigauke, C., & Bere, A. (2017). Modelling non-stationary time series using a peaks over threshold dis-tribution with time varying covariates and threshold: An application to peak electricity de-mand. Energy, 119, 152-166.Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805.
Soares, L. J., & Souza, L. R. (2006). Forecasting electricity demand using generalized long memory. International Journal of Forecasting, 22(1), 17-28.
Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and sustainable energy reviews, 16(2), 1223-1240.
Toksarı, M. D. (2009). Estimating the net electricity energy generation and demand using the ant colo-ny optimization approach: case of Turkey. Energy Policy, 37(3), 1181-1187.
Von Hirschhausen, C., & Andres, M. (2000). Long-term electricity demand in China—from quantita-tive to qualitative growth?. Energy policy, 28(4), 231-241.
Wang, Y., Wang, J., Zhao, G., & Dong, Y. (2012). Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China. Energy Policy, 48, 284-294.
Wang, H., Zhu, S., Zhao, J., & Li, G. (2010, December). An improved combined model for the electric-ity demand forecasting. In Computational and Information Sciences (ICCIS), 2010 International Conference on (pp. 108-111). IEEE.
Yang, M., & Yu, X. (2004). China’s rural electricity market—a quantitative analysis. Energy, 29(7), 961-977
Amarawickrama, H. A., & Hunt, L. C. (2008). Electricity demand for Sri Lanka: a time series analy-sis. Energy, 33(5), 724-739.
Awan, S. M., Khan, Z. A., Aslam, M., Mahmood, W., & Ahsan, A. (2012, May). Application of NARX based FFNN, SVR and ANN Fitting models for long term industrial load forecasting and their comparison. In Industrial Electronics (ISIE), 2012 IEEE International Symposium on (pp. 803-807). IEEE.
Azadeh, A., Neshat, N., Rafiee, K., & Zohrevand, A. M. (2012). An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: the case of Iran. Transportation Planning and Technology, 35(2), 221-240.
Barakat, E. E., & Al-Rashed, S. A. (1992). Long range peak demand forecasting under conditions of high growth. IEEE Transactions on Power Systems, 7(4), 1483-1486.
Battistelli, C., Carpinelli, G., Mottola, F., Proto, D., & Zoppoli, D. (2011). Distribution networks with linear and nonlinear loads: cost-based method for planning dispersed generation and capacitor units under uncertainty. Journal of Energy Engineering, 137(1), 44-58.
Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421.
Camara, A., Feixing, W., & Xiuqin, L. (2016). Energy consumption forecasting using seasonal ARIMA with artificial neural networks models. International Journal of Business and Management, 11(5), 231.
Connor, J. D. (1987). The demand for electricity in western US irrigated agriculture: a dual cost func-tion analysis.
Daim, T. U., & Oliver, T. (2008). Implementing technology roadmap process in the energy services sec-tor: A case study of a government agency. Technological Forecasting and Social Change, 75(5), 687-720.
De Castro, J. B., & de Ávila Montini, A. (2010). FORECASTING RESIDENTIAL ELECTRICITY CONSUMPTION IN BRAZIL: APPLICATION OF THE ARX MODEL. Future Studies Research Journal: Trends and Strategies, 2(2), 3-16.
Dilaver, Z., & Hunt, L. C. (2011). Industrial electricity demand for Turkey: a structural time series analysis. Energy Economics, 33(3), 426-436.
Durán, M. J., Cros, D., & Riquelme, J. (2007). Short-term wind power forecast based on ARX mod-els. Journal of Energy Engineering, 133(3), 172-180.
Elkarmi, F. (2008). Load research as a tool in electric power system planning, operation, and control—The case of Jordan. Energy Policy, 36(5), 1757-1763.
Gabriel, S. A., Sahakij, P., & Balakrishnan, S. (2004). Optimal retailer load estimates using stochastic dynamic programming. Journal of Energy Engineering, 130(1), 1-17.
Ghanbari, A., Kazemi, S. M., Mehmanpazir, F., & Nakhostin, M. M. (2013). A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowledge-Based Systems, 39, 194-206.
Glasnovic, Z., & Margeta, J. (2010). Sustainable electric power system: Is it possible? Case study: Croa-tia. Journal of Energy Engineering, 136(4), 103-113.
Haas, R., & Schipper, L. (1998). Residential energy demand in OECD-countries and the role of irre-versible efficiency improvements. Energy economics, 20(4), 421-442.
Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009). Electric load forecasting methods: Tools for decision making. European journal of operational research, 199(3), 902-907.
Kani, S. A. P., & Ershad, N. F. (2007, October). Annual electricity demand prediction for Iranian agri-culture sector using ANN and PSO. In Electrical Power Conference, 2007. EPC 2007. IEEE Cana-da (pp. 446-451). IEEE.
Kazemi, A., Shakouri, H. G., Menhaj, M. B., Mehregan, M. R., & Neshat, N. (2011). A hierarchical fuzzy linear regression model for forecasting agriculture energy demand: A case study of Iran. In 2011 3rd International Conference on Information and Financial Engineering (Vol. 12, pp. 19-24).
Kucukali, S., & Baris, K. (2010). Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach. Energy Policy, 38(5), 2438-2445.
Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137, 47-56.
Magnano, L., & Boland, J. W. (2007). Generation of synthetic sequences of electricity demand: Appli-cation in South Australia. Energy, 32(11), 2230-2243.
Neto, A. H., & Fiorelli, F. A. S. (2008). Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and buildings, 40(12), 2169-2176.
Newsham, G. R., & Birt, B. J. (2010, November). Building-level occupancy data to improve ARIMA-based electricity use forecasts. In Proceedings of the 2nd ACM workshop on embedded sensing sys-tems for energy-efficiency in building (pp. 13-18). ACM.
Nocedal, J., & Wright, S. (2006). Numerical optimization, series in operations research and financial en-gineering. Springer, New York, USA, 2006.
Nwobi-Okoye, C. C., & Igboanugo, A. C. (2015). Performance appraisal of gas based electric power generation system using transfer function modelling. Ain Shams Engineering Journal, 6(2), 541-551.
Pao, H. T. (2009). Forecasting energy consumption in Taiwan using hybrid nonlinear mod-els. Energy, 34(10), 1438-1446.
Porkar, S., Poure, P., Abbaspour-Tehrani-fard, A., & Saadate, S. (2010). A novel optimal distribution system planning framework implementing distributed generation in a deregulated electricity mar-ket. Electric Power Systems Research, 80(7), 828-837.
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.
Sarduy, J. R. G., Di Santo, K. G., & Saidel, M. A. (2016). Linear and non-linear methods for prediction of peak load at University of São Paulo. Measurement, 78, 187-201.
Sharma, D. P., Nair, P. C., & Balasubramanian, R. (2002). Demand for commercial energy in the state of Kerala, India: an econometric analysis with medium-range projections. Energy policy, 30(9), 781-791.
Sigauke, C., & Bere, A. (2017). Modelling non-stationary time series using a peaks over threshold dis-tribution with time varying covariates and threshold: An application to peak electricity de-mand. Energy, 119, 152-166.Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805.
Soares, L. J., & Souza, L. R. (2006). Forecasting electricity demand using generalized long memory. International Journal of Forecasting, 22(1), 17-28.
Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and sustainable energy reviews, 16(2), 1223-1240.
Toksarı, M. D. (2009). Estimating the net electricity energy generation and demand using the ant colo-ny optimization approach: case of Turkey. Energy Policy, 37(3), 1181-1187.
Von Hirschhausen, C., & Andres, M. (2000). Long-term electricity demand in China—from quantita-tive to qualitative growth?. Energy policy, 28(4), 231-241.
Wang, Y., Wang, J., Zhao, G., & Dong, Y. (2012). Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China. Energy Policy, 48, 284-294.
Wang, H., Zhu, S., Zhao, J., & Li, G. (2010, December). An improved combined model for the electric-ity demand forecasting. In Computational and Information Sciences (ICCIS), 2010 International Conference on (pp. 108-111). IEEE.
Yang, M., & Yu, X. (2004). China’s rural electricity market—a quantitative analysis. Energy, 29(7), 961-977