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

A linear regression-based model for the calculation of short-term system load forecasts is described. The model's most significant aspects fall into the following areas: innovative model building, including accurate holiday modeling by using binary variables and 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; 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 model was tested under a wide variety of conditioning and is shown to produce excellent results.>