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Short term load forecasting has an essential medium for the reliable,economical and efficient operation of the power system. Most of the existingforecasting approaches utilize fixed statistical models with large historicaldata for training the models. However, due to the recent integration of largedistributed generation, the nature of load demand has become dynamic. Thusbecause of the dynamic nature of the power load demand, the performance ofthese models may deteriorate over time. To accommodate the dynamic nature ofthe load demands, we propose a sliding window regression based dynamic model topredict the load demands of the multiarea power system. The proposed algorithmis tested on five zones of New York ISO. Results from our proposed algorithmare compared with four existing techniques to validate the performancesuperiority of the proposed algorithm.

Sliding Window Regression based Short-Term Load Forecasting of a Multi-Area Power System