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Hierarchical Load Hindcasting Using Reanalysis Weather | IEEE Journals & Magazine | IEEE Xplore

Hierarchical Load Hindcasting Using Reanalysis Weather


Abstract:

By leveraging recent advances in atmospheric reanalysis it is possible to more fully characterize the effects of low frequency weather phenomena simultaneously affecting ...Show More

Abstract:

By leveraging recent advances in atmospheric reanalysis it is possible to more fully characterize the effects of low frequency weather phenomena simultaneously affecting the native load and power output of weather-sensitive generators. To this end, this paper describes load “hindcasting”-a method of using reanalysis data to re-synthesize multiple decades of historical load data such that it represents a current and consistent load profile. When used together with coincident, reanalysis-derived records of weather-sensitive power output, load hindcasting enables a robust, long-term characterization of these resources that accounts for weather variability spanning decades. Drawing from the field of short-term load forecasting, hierarchical load hindcasting models are developed for summer weekday hours in New England using weather variables from the Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset developed by the National Aeronautics and Space Administration (NASA). Results demonstrate the efficacy of hindcasting realistic hourly loads using MERRA.
Published in: IEEE Transactions on Smart Grid ( Volume: 5, Issue: 1, January 2014)
Page(s): 447 - 455
Date of Publication: 27 September 2013

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