{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/124443"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/124443","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Comparative Analysis of Machine Learning Models for ERCOT Short Term Load Forecasting","abstract":"This study investigates the efficacy of various machine learning (ML) and deep learning (DL) models for short-term load forecasting (STLF) in the Electric Reliability Council of Texas (ERCOT) grid. A dual comparative approach is employed, evaluating models based on temporal features alone as well as in combination with actual and forecasted weather variables. The research emphasizes region-specific forecasting by capturing heterogeneous load patterns for ERCOT's individual weather zones and aggregating them to predict total load. 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