  Short term load forecasting has an essential medium for the reliable,
economical and efficient operation of the power system. Most of the existing
forecasting approaches utilize fixed statistical models with large historical
data for training the models. However, due to the recent integration of large
distributed generation, the nature of load demand has become dynamic. Thus
because of the dynamic nature of the power load demand, the performance of
these models may deteriorate over time. To accommodate the dynamic nature of
the load demands, we propose a sliding window regression based dynamic model to
predict the load demands of the multiarea power system. The proposed algorithm
is tested on five zones of New York ISO. Results from our proposed algorithm
are compared with four existing techniques to validate the performance
superiority of the proposed algorithm.
