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Activity Number: 419
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #305981
Title: Uncertainty Assessments of a Daily Electricity System Demand Estimation Model
Author(s): Cha-Chi Fan*+
Companies: U.S. Energy Information Administration
Address: 3010 Duncross Lane, Dunkirk, MD, 20754, United States
Keywords: uncertainty analysis ; sensitivity analysis ; bulk power system

Understanding input and prediction uncertainty of models may provide information on not only the risk of making incorrect/correct decisions based on models but also about the direction of future model revisions. Numeric models, based on machine learning techniques, are widely used to estimate and predict complex bulk power system demand. These models "learn" from the training data to fit the system demand closely based on several known properties, such as system peak demand, weather, seasonality, and consumer characteristics. High accuracy of model estimations is desired due to industrial reliability concerns and high public interest. However, model projections always contain inherent statistical errors and uncertainties. Uncertainty in the numeric imitations of the bulk power system from these models is especially hard to quantify with ordinary approaches. In this study, we discuss several possible solutions and would like to emphasize the importance of understanding input and prediction uncertainty of a daily electricity system demand estimation model.

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