Load forecasting is vitally important for the electric industry in the deregulated economy. It hasmany applications including energy purchasing and generation, load switching, contractevaluation and infrastructure development. Because of this, a large variety of mathematicalmethods have been developed for load forecasting.In addition, the large-scale integration of wind power, nowadays, represents a challenge forpower system operations planning, because wind power cannot be dispatched in a classicalsense. Moreover, its output varies as weather conditions change. This warrants theinvestigation of alternative short-term power system planning methods capable of better copingwith the nature of wind generation.This paper focuses on carrying out an accurate short-term load forecasting method, in a mixedscenario with uncertain wind power generation (WPG) and load, improving forecasting errorsobtained with different algorithms tested. It focuses only in regression models, although othermethods have already been developed, such as systems of artificial intelligence.

Projecte fet en col.laboració amb l'Instituto Superior Tecnico. Universidade Técnica de Lisboa