Electricity Load Modeling: An Application to Italian Market

Forecasting electricity load plays a crucial role regards
decision making and planning for economical purposes. Besides, in
the light of the recent privatization and deregulation of the power
industry, the forecasting of future electricity load turned out to be a
very challenging problem. Empirical data about electricity load
highlights a clear seasonal behavior (higher load during the winter
season), which is partly due to climatic effects. We also emphasize
the presence of load periodicity at a weekly basis (electricity load is
usually lower on weekends or holidays) and at daily basis (electricity
load is clearly influenced by the hour). Finally, a long-term trend may
depend on the general economic situation (for example, industrial
production affects electricity load). All these features must be
captured by the model.
The purpose of this paper is then to build an hourly electricity load
model. The deterministic component of the model requires non-linear
regression and Fourier series while we will investigate the stochastic
component through econometrical tools.
The calibration of the parameters’ model will be performed by
using data coming from the Italian market in a 6 year period (2007-
2012). Then, we will perform a Monte Carlo simulation in order to
compare the simulated data respect to the real data (both in-sample
and out-of-sample inspection). The reliability of the model will be
deduced thanks to standard tests which highlight a good fitting of the
simulated values.




References:
[1] Afshar K. and Bigdeli N. (2011) "Data analysis and short term load
forecasting in Iran electricity market using singular spectral analysis
(SSA)", Energy, Vol. 36, pp. 2620-2627.
[2] Alter N. and Syed H. S. (2011) “An empirical analysis of electricity
demand in Pakistan”, International Journal of Energy Economics and
Policy, Vol. 1, No. 4, pp. 116-139.
[3] Andersson G., Cornel J., Hezog F., Hildmann, M. and Stokic, D. (2011)
“Robust calculation and parameter estimation of the Hourly Price
Forward Curve”, 17th Power Systems Computation Conference, August
22-26, 2011, Stockholm, Sweden.
[4] Andersson G., He, Y., Hildmann M. and Sotiropoulos E. (2013)
“Modeling of Electricity Load for Forward Contract Pricing”, Institute
of Electrical and Electronics Engineers, IEEE, July 21, 2013.
[5] Bianco V., Manca O. and Nardini S. (2009) “Electricity consumption
forecasting in Italy using Linear Regression Models”, Energy, Vol. 34,
pp. 1413-1421.
[6] Bilgili M., Sahin B., Yasar A. and Simsek E. (2012) “Electric energy
demands of Turkey in residential and industrial sectors”, Renewable and
Sustainable Energy Reviews, Vol. 16, pp. 404-414.
[7] Blàzquez L., Nina B. and Filippini M. (2012) “Residential electricity
demand for Spain: new empirical evidence using aggregated data”,
Centre for Energy Policy and Economics, Swiss Federal Institutes of
Technology, CEPE working paper No. 82.
[8] Bruhns A., Deurveilher G., and Roy J. S. “A non-linear regression
model for mid-term load forecasting and improvements in seasonality”,
15th PSCC, Liege, 22-26 August 2005.
[9] Chujai P., Kerdprasop N. and Kerdprasop K. “Time series analysis of
household electric consumption with ARIMA and ARMA models”,
IMECS 2013, March 13-15, 2013, Hong Kong.
[10] Collet J., Dessertaine A., Dordonnat V., Koopman S. J. And Ooms M.,
(2008) “Journal of Forecasting”, Vol. 24, pp. 566-587.
[11] Deihimi A., Orang O. and Showkati H. (2012) "Short-term electric load
and temperature forecasting using wavelet echo state networks with
neural reconstruction" , Energy, Vol. 57, pp. 382-401.
[12] Fan S. and Hyndman R. J. (2012) “Short-term load forecasting based on
a semi-parametric additive model”, IEEE Transactions on Power
Systems, Vol. 27, No. 1, pp. 134-141.
[13] Filik U. B., Gerek Ö., N. and Kurban M. (2011) “A novel modeling
approach for hourly forecasting of long-term electric energy demand”,
Energy Conversion and Management, Vol. 52, pp. 199-211.
[14] Gonzàles-Romera E., Jaramillo-Moràn M. A. and Carmona-Fernàndez
D. (2008) "Monthly electric energy demand forecasting with neutral
networks and Fourier series", Energy Conversion and Management, Vol.
49, pp. 3135-3142.
[15] Hong W-C (2011) "Electric load forecasting by seasonal recurrent SVR
(support vector regression) with chaotic artificial bee colony algorithm",
Energy, Vol. 36, pp. 5568-5578.
[16] Kavousian A., Rajagopal R. and Fischer M. (2013) “Determinants of
residential electricity consumption: using smart meter data to examine
the effect of climate, building characteristics, appliance stock, and
occupants’ behavior”, Energy, Vol. 55, pp. 184-194.
[17] Makridakis S., Wheelwright S. C., and Hyndman R. J. (1998)
“Forecasting: methods and applications” New York: John Wiley &
Sons.
[18] Migon H. S., and Alves L. C., (2013) “Multivariate dynamic regression:
modeling and forecasting for intraday electricity load”, Applied
Stochastic Models in Business and Industry, Vol. 29, pp. 579-598.
[19] Moral-Carcedo J. and Vicéns-Otero J. (2005) “Modelling the non-linear
response of Spanish electricity demand to temperature variations”,
Energy Economics, Vol. 27, Issue 3, pp. 477-494.
[20] Nagi J., Yap K.S., Tiong S.K., Ahmed, S.K. “Electrical power load
forecasting using hybrid self-organizing maps and support vector
machines”, The 2nd International Power Engineering and Optimization
Conference (PEOCO 2008), Shah Alam, Selangor, Malaysia, 4-5 June
2008.
[21] Pardo, A. Meneu V., and Valor E. (2002) “Temperatures and seasonality
influences on Spanish electricity load”, Energy Economics, Vol. 24, pp.
55-70.
[22] Pielow A, Sioshansi R. and Roberts M.C. (2012) “Modeling short-run
electricity demand with long-term growth rates and consumer price
elasticity in commercial and industrial sectors”, Energy, Vol. 46,
pp.533-540.
[23] Saab S., Badr E., and Nasr G. (2001) “Univariate modeling and
forecasting of energy consumption: the case of electricity in Lebanon”,
Energy, Vol. 26, pp. 1-14.
[24] Soares L. J. and Medeiros M. (2008) “Modeling and forecasting shortterm
electricity load: A comparison of methods with an application to
Brazilian data”, International Journal of Forecasting, Vol. 24, pp. 630-
644.
[25] Wang C-H., Grozev G. and Seo S. (2012) "Decomposition and statistical
analysis for regional electricity demand forecasting", Energy, Vol. 41,
pp. 313-325.
[26] Weron R., (2006), “Modeling and forecasting electricity loads and
prices. A statistical approach”, John Wiley & Sons Ltd.
[27] http://www.mercatoelettrico.org/En/Default.aspx
[28] http://www.istat.it/en