Knowledge enhanced connectionist models for short-term electric load forecasting

Abstract

This paper addresses short-term load forecasting using machine learning and neural network techniques. Neural networks, though accurate in weekday load forecasting, are poor at forecasting maximum daily load, weekend and holiday loads. This necessitates development of a robust forecasting technique to complement the neural networks for enhanced reliability of forecast and improved overall accuracy. The statistical decision tree method produces robust forecasts and human intelligible rules. These rules provide understanding of factors driving load demand. Decision trees when combined with neural network forecasts, produce robust and accurate forecasts. Simulations are performed on a service area susceptible to large and sudden changes in weather and load. Forecasts obtained by the proposed method are accurate under diverse conditions.<>