A regression-based approach to short-term system load forecasting

The authors describe a novel linear regression-based model for the calculation of short-term system load forecasts. The model´s most significant new aspects fall into the following areas: innovative model building, including accurate holiday modeling by using binary variables; temperature modeling by using heating and cooling degree functions; robust parameter estimation and parameter estimation under heteroskedasticity by using weighted least-squares linear regression techniques; the use of ´reverse errors-in-variables´ techniques to mitigate the effects on load forecasts of potential errors in the explanatory variables; and distinction between time-independent daily peak load forecasts and the maximum of the hourly load forecasts in order to prevent peak forecasts from being negatively biased. The significant impact of these issues on the accuracy of a model´s results was established through testing of an existing load forecasting algorithm. The model has been tested under a variety of conditions and it was shown to produce excellent results. It is also sufficiently general to be used by other electric power utilities.<>