Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach
Regional electricity demand in Japan and spatial interaction among the regions using a Bayesian approach were examined. A spatial autoregressive (SAR) ARMA model was proposed to consider the features of electricity demand in Japan and a strategy of Markov chain Monte Carlo (MCMC) methods was constructed to estimate the parameters of the model. From empirical results, the spatial autoregressive ARMA (1, 1) model was selected, and it was found that spatial interaction plays an important role in electricity demand in Japan. Moreover, log predictive density showed that this SAR-ARMA model performs better than a univariate ARMA model. It was confirmed that the space-time model improves the performance of forecasting future electricity demand in Japan.
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- Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
- Xiao, Ni & Zarnikau, Jay & Damien, Paul, 2007. "Testing functional forms in energy modeling: An application of the Bayesian approach to U.S. electricity demand," Energy Economics, Elsevier, vol. 29(2), pages 158-166, March.
- Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
- Niels Haldrup & Morten O. Nielsen, 2004.
"A Regime Switching Long Memory Model for Electricity Prices,"
Economics Working Papers
2004-2, Department of Economics and Business Economics, Aarhus University.
- Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376.
- John F. Geweke, 1998.
"Using simulation methods for Bayesian econometric models: inference, development, and communication,"
249, Federal Reserve Bank of Minneapolis.
- John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
- Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008.
"An hourly periodic state space model for modelling French national electricity load,"
International Journal of Forecasting,
Elsevier, vol. 24(4), pages 566-587.
- V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.
- Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
- Smirnov, Oleg & Anselin, Luc, 2001. "Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach," Computational Statistics & Data Analysis, Elsevier, vol. 35(3), pages 301-319, January.
- Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
- repec:rim:rimwps:22-08 is not listed on IDEAS
- Gelfand, Alan E. & Banerjee, Sudipto & Sirmans, C.F. & Tu, Yong & Eng Ong, Seow, 2007. "Multilevel modeling using spatial processes: Application to the Singapore housing market," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3567-3579, April.
- Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
- Rong Chen & John L. Harris & Jun M. Liu & Lon-Mu Liu, 2006. "A semi-parametric time series approach in modeling hourly electricity loads," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(8), pages 537-559.
- Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
- Holloway, Garth & Shankar, Bhavani & Rahman, Sanzidur, 2002.
"Bayesian spatial probit estimation: a primer and an application to HYV rice adoption,"
Agricultural Economics of Agricultural Economists,
International Association of Agricultural Economists, vol. 27(3), November.
- Holloway, Garth & Shankar, Bhavani & Rahman, Sanzidur, 2002. "Bayesian spatial probit estimation: a primer and an application to HYV rice adoption," Agricultural Economics, Blackwell, vol. 27(3), pages 383-402, November.
- Pace, R. Kelley & LeSage, James P., 2004. "Chebyshev approximation of log-determinants of spatial weight matrices," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 179-196, March.
- Cottet R. & Smith M., 2003. "Bayesian Modeling and Forecasting of Intraday Electricity Load," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 839-849, January.
- Erdogdu, Erkan, 2007.
"Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey,"
Elsevier, vol. 35(2), pages 1129-1146, February.
- Erdogdu, Erkan, 2007. "Electricity Demand Analysis Using Cointegration and ARIMA Modelling: A case study of Turkey," MPRA Paper 19099, University Library of Munich, Germany.
- Nakatsuma, Teruo, 2000. "Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach," Journal of Econometrics, Elsevier, vol. 95(1), pages 57-69, March.
- Joanna Nowicka-Zagrajek & Rafal Weron, 2002. "Modeling electricity loads in California: ARMA models with hyperbolic noise," HSC Research Reports HSC/02/02, Hugo Steinhaus Center, Wroclaw University of Technology.
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