Analysis and evaluation of five short-term load forecasting techniques

Abstract

A review of five widely applied short-term (up to 24 h) load forecasting techniques is presented. These are: multiple linear regression; stochastic time series; general exponential smoothing; state space and Kalman filter; and a knowledge-based approach. A brief discussion of each of these techniques, along with the necessary equations, is presented. Algorithms implementing these forecasting techniques have been programmed and applied to the same database for direct comparison of these different techniques. A comparative summary of the results is presented to give an understanding of the inherent level of difficulty of each of these techniques and their performances.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>