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

2019/05/20 by Irfan Khan, Khan, Irfan Ahmad, Adnan Akber +3
Decision Sciences · Engineering · #Electric Power System Optimization #Energy Load and Power Forecasting #FOS: Electrical engineering #Signal Processing (eess.SP) #Stock Market Forecasting Methods #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1905.08111

openalex publication_date 2019/05/20 · openalex created_date 2019/05/29 · openalex updated_date 2026/07/28

Abstract

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.

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