Weather and Random Forest-based Load Profiling Approximation Models and Their Transferability across Climate Zones

Zhou, Huifen
Hou, Z. Jason
Liu, Yuan
Etingov, Pavel
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This study is to provide predictive understanding of the associations of weather attributes with electricity load profiles across a variety of climate zones and seasons. Firstly, machine learning (ML) approaches were used to identify and quantify the impacts of various weather attributes on residential and commercial electricity demand and its components across the western United States. Performance and transferability of the developed ML models were then evaluated across different temperate zones (e.g., southern, middle, and northern US) and across coastal, mid-continent, and wet zones, with inputs of weather condition data from the National Oceanic and Atmospheric Administration (NOAA) at representative weather stations. The predictive models were developed based on the ranked and screened factors using the regression tree (RT) and random forest (RF) approaches, for five different scenarios (seasons).
Policy, Markets and Analytics, load composite, load profiling, machine learning, model transferability
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