Modeling special-day effects for forecasting intraday electricity demand

We propose and apply a novel approach for modeling special-day effects to predict electricity demand in Korea. Notably, we model special-day effects on an hourly rather than a daily basis. Hourly specified predictor variables are implemented in the regression model with a seasonal autoregressive moving average (SARMA) type error structure in order to efficiently reflect the special-day effects. The interaction terms between the hour-of-day effects and the hourly based special-day effects are also included to capture the unique intraday patterns of special days more accurately. The multiplicative SARMA mechanism is employed in order to identify the double seasonal cycles, namely, the intraday effect and the intraweek effect. The forecast results of the suggested model are evaluated by comparing them with those of various benchmark models for the following year. The empirical results indicate that the suggested model outperforms the benchmark models for both special- and non-special day predictions.

[1]  M. Medeiros,et al.  Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data , 2008 .

[2]  Silja Meyer-Nieberg,et al.  Electric load forecasting methods: Tools for decision making , 2009, Eur. J. Oper. Res..

[3]  Haipeng Shen,et al.  Interday Forecasting and Intraday Updating of Call Center Arrivals , 2008, Manuf. Serv. Oper. Manag..

[4]  James W. Taylor,et al.  A Comparison of Univariate Time Series Methods for Forecasting Intraday Arrivals at a Call Center , 2008, Manag. Sci..

[5]  Derek W. Bunn,et al.  Large neural networks for electricity load forecasting: Are they overfitted? , 2005 .

[6]  Á. Pardo,et al.  Temperature and seasonality influences on Spanish electricity load , 2002 .

[7]  James W. Taylor,et al.  Triple seasonal methods for short-term electricity demand forecasting , 2010, Eur. J. Oper. Res..

[8]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[9]  José Ramón Cancelo,et al.  Forecasting the electricity load from one day to one week ahead for the Spanish system operator , 2008 .

[10]  P. McSharry,et al.  A comparison of univariate methods for forecasting electricity demand up to a day ahead , 2006 .

[11]  Remy Cottet,et al.  Bayesian Modeling and Forecasting of Intraday Electricity Load , 2003 .

[12]  Jonathan Weinberg,et al.  Bayesian Forecasting of an Inhomogeneous Poisson Process With Applications to Call Center Data , 2007 .

[13]  Georges A. Darbellay,et al.  Forecasting the short-term demand for electricity: Do neural networks stand a better chance? , 2000 .

[14]  Rob J Hyndman,et al.  Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing , 2011 .

[15]  Yong Cheol Kang,et al.  Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method , 2000 .

[16]  M. Smith.  Modeling and Short-Term Forecasting of New South Wales Electricity System Load , 2000 .

[17]  Siem Jan Koopman,et al.  Periodic Seasonal Reg-ARFIMA–GARCH Models for Daily Electricity Spot Prices , 2007 .

[18]  J. W. Taylor,et al.  Short-Term Load Forecasting With Exponentially Weighted Methods , 2012, IEEE Transactions on Power Systems.

[19]  J. W. Taylor,et al.  Short-term electricity demand forecasting using double seasonal exponential smoothing , 2003, J. Oper. Res. Soc..

[20]  A. C. Liew,et al.  Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting , 1995 .

[21]  J. Nowicka-Zagrajek,et al.  Modeling electricity loads in California: ARMA models with hyperbolic noise , 2002, Signal Process..

[22]  Rob J. Hyndman,et al.  Forecasting time series with multiple seasonal patterns , 2008, Eur. J. Oper. Res..

[23]  R. Ramanathan,et al.  Short-run forecasts of electricity loads and peaks , 1997 .