Electrical energy demand modeling: beyond normality
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.3856Keywords:
energy demand, semi-nonparametric modelling, energy market, quantile risk metricsAbstract
This work proposes a model of electrical energy demand based on time series methods and semi-nonparametric statistics (SNP). This allows knowing not only the expected value of the demand but also its probability distribution so that, by calculating metrics such as the Quantile Risk Metrics, decisions can be made based on less or more extreme values favorable than the expected value. The results show that in the case of electricity demand in the Colombian market between 2000 and 2018, the probability distribution of the average daily demand is leptokurtic. That is, extreme events occur more frequently than those assumed by a normal distribution. Thus, the Gaussian distribution assumption leads to undervaluation of risk in terms of undervaluation of the frequency of extreme values.
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Andalib, A., & Atry, F. (2009). Multi-step ahead forecasts for electricity prices using NARX: a new approach, a critical analysis of one-step ahead forecasts. Energy Conversion Management, 50(3), 739-747. https://doi.org/10.1016/j.enconman.2008.09.040
Beenstock, M., Goldin, E., & Nabot, D. (1999). The demand for electricity in Israel. Energy Economics, 21(2), 168-183.
Bentzen, J., & Engsted, T. (1993). Short- and long-run elasticities in energy demand: a cointegration approach. Energy Economics, 15(1), 9-16.
Bentzen, J., & Engsted, T. (2001). A revival of the autoregressive distributed lag model in estimating energy demand relationships. Energy, 26(1), 45-55.
Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421. https://doi.org/10.1016/j.energy.2009.06.034
Bollerslev, T., & Wooldridge, J. (1992). Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econometric Reviews, 11, 143-172.
Brunner, A.D. (1992). Conditional asymmetries in real GNP: A Seminonparametric Approach. Journal of Business & Economic Statistics, 10(1), 65-72. https://doi.org/10.2307/1391805
Callaway, D.S. (2010). Sequential Reliability Forecasting for Wind Energy: Temperature Dependence and Probability Distributions. IEEE Transactions on Energy Conversion, 25(2), 577-585. https://doi.org/ 10.1109/TEC.2009.2039219.
Cortés, L.M., Mora-Valencia, A., & Perote, J. (2018). Retrieving the implicit risk neutral density of WTI options with a semi-nonparametric approach. The North American Journal of Economics and Finance. In press. https://doi.org/10.1016/j.najef.2018.10.010
Debnath, K.B., & Mourshed, M. (2018). Forecasting methods in energy planning models. Renewable and Sustainable Energy Reviews, 88(C), 297-325.
Del Brio, E., & Perote, J. (2012). Gram-Charlier densities: Maximum likelihood versus the method of moments. Insurance: Mathematics and Economics, 51(3), 531-537. https://doi.org/ 10.1016/j.insmatheco.2012.07.005
Domeett, G. (2015). Análisis de los determinantes del cambio de la demanda de energía eléctrica en la ciudad de Neuquén. Ciencias Administrativas, 3(6), 1-15. https://revistas.unlp.edu.ar/CADM/article/view/1541
Ediger, V., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35, 1701-1708.
El-Desouky, A.A. & El-Kateb, M.M. (2000). Hybrid adaptive techniques for electric-load forecast using ANN and ARIMA. IEE Proceedings: Generation, Transmission and Distribution, 147(4), 213-217.
Engle, R.F., Granger, C.W.J., & Hallman, J.J. (1989). Merging short-and long-run forecasts: an application of seasonal cointegration to monthly electricity sales forecasting. Journal of Econometrics, 40(1), 45-62.
Erdougdu, E. (2007). Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey. Energy Policy, 35, 1129-1146.
Fouquet, R., Hawdon, D., Pearson, P., Robinson, C., & Stevens, P. (1993). The SEEC United Kingdom energy demand forecast. Surrey Energy Economics Centre (SEEC): Occasional Paper 1, Department of Economics, University of Surrey, Guildford, UK.
González-Romera, E., Jaramillo-Morán, M.A., & Carmona-Fernández, D. (2006). Monthly electric energy demand forecasting based on trend extraction. IEEE Transactions Power Systems, 21(4), 1946-1953. https://doi.org/10.1109/TPWRS.2006.883666
Hull, J.C. (2002). Introducción a los Mercados de Futuros y Opciones. Boston: Prentice Hall.
Hunt, L.C., & Witt, R. (1995). An analysis of UK energy demand using multivariate cointegration. Surrey Energy Economics Centre (SEEC): Discussion Paper No: 86, Department of Economics, University of Surrey, Guildford, UK.
Hussain, A., Rahman, M., & Memon, J.A. (2016). Forecasting electricity consumption in Pakistan: the way forward. Energy Policy, 90, 73-80.
Ioannou, A., Angus, A., & Brennan, F. (2017). Risk-based methods for sustainable energy system planning: A review. Renewable and Sustainable Energy Reviews, 74, 602-615.
Jondeau, E., & Rockinger, M. (2001). Gram-Charlier densities. Journal of Economic Dynamics & Control, 25(10), 1457-1483. https://doi.org/10.1016/s0165-1889(99)00082-2
Kupiec, P. (1995). Techniques for verifing the accuracy of risk measurement models. The Journal of Derivatives, 3, 71-84.
León, A., Mencia, J., & Sentana, E. (2007). Parametric Properties of Semi-Nonparametric Distributions, with Applications to Option Valuation. Documentos de trabajo No 0707. Madrid, España: Banco de España.
Lucia, J.J., & Schwartz, E.S. (2002). Electricity prices and power derivatives: Evidence from the Nordic power exchange. Review of Derivatives Research, 5(1), 5-50. https://doi.org/10.1023/A:1013846631785
Mauleon, I., & Perote, J. (2000). Testing densities with financial data: An Empirical comparison of the Edgeworth-Sargan density to the Student's t. The European Journal of Finance, 6(2), 225-239. https://doi.org/10.1080/13518470050020851
Melikoglu, M. (2013). Forecasting Turkey's natural gas demand between 2013 and 2030. Renew Sustain Energy Reviews, 22, 393-400.
Nasr, G., Badr, E., & Dibeh, G. (2000). Econometric modelling of electricity consumption in postwar Lebanon. Energy Economics, 22(6), 627-640.
Ñíguez, T.-M., & Perote, J. (2011). Forecasting havy-tailed densities with positive Edgeworth and Gram-Charlier expansions. Oxford Bulletin of Economics and Statics, 74(4), 600-627. https://doi.org/10.1111/j.1468-0084.2011.00663.x
UPME (2016). Seguridad energética para Colombia. Entregable 3: Informe Final. https://bdigital.upme.gov.co/bitstream/001/1314/1/Seguridad%20Energ%C3%A9tica%20UPME-CIDET%20Entrega%20Final.pdf
Trespalacios, A., Cortés, L., & Perote, J. (2020). Uncertainty in electricity markets from a semi-nonparametric approach. Energy Policy, 137, 111091.
Tsay, R.S. (2005). Analysis of Financial Time Series. Vol. 543. Chicago: John Wiley & sons.
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.
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