UC San Diego

This paper provides some new techniques to predict the electric load using Multiple Linear Regression (MLR) model, which adopts a statistical approach assuming that the past load and weather data have an information for forecasting the target load. Since the conventional general MLR prediction performance can be degraded by seasonal effects, we propose some new MLR techniques to improve the prediction performance. We have found that the performance of the proposed MLR can be further improved by solving weighted least squares problem and clustering the training set. Also, we try to compare the prediction performances for such techniques to find out the best. Our argument will be demonstrated by the two case studies with real electric and weather data.