Campus Units

Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

5-15-2016

Journal or Book Title

Applied Energy

Volume

170

First Page

455

Last Page

465

DOI

10.1016/j.apenergy.2016.02.118

Abstract

Short-term load forecasting is important for power system generation planning and operation. For unit commitment and dispatch processes to incorporate uncertainty, a short-term load model must not only provide accurate load predictions but also enable the generation of reasonable probabilistic scenarios or uncertainty sets. This paper proposes a temporal and weather conditional epi-splines based load model (TWE) using functional approximation. TWE models the dependence of load on time and weather separately by functional approximation using epi-splines, conditional on season and area, in each segment of similar weather days. Load data are transformed from various day types to a specified reference day type among similar weather days in the same season and area, in order to enrich the data for capturing the non-weather dependent load pattern. In an instance derived from an Independent System Operator in the U.S., TWE not only provides accurate hourly load prediction and narrow bands of prediction errors, but also yields serial correlations among forecast hourly load values within a day that are similar to those of actual hourly load.

Comments

NOTICE: this is the author’s version of a work that was accepted for publication in Applied Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Energy, v.170, 15 May (2016): 455, doi: 10.1016/j.apenergy.2016.02.118.

Copyright Owner

Elsevier Ltd.

Language

en

File Format

application/pdf

Published Version

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