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Activity Number: 414
Type: Topic Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #316631 View Presentation
Title: Uncertainty Estimation in Electricity Demand Forecasting
Author(s): Bei Chen* and Mathieu Sinn
Companies: IBM Research and IBM Research
Keywords: uncertainty ; forecasting ; energy demand
Abstract:

Quantifying uncertainty in electricity demand is of paramount importance for utility company operations and energy market trading. Due to the nature of electricity demand, the forecasting residuals are typically heteroscedastic and leptokurtic. In this talk, we propose an approach based on the Generalized Additive Model (GAM) to estimate the conditional variance of electricity demand. We provide results from numerical experiment and comparisons to state-of-the-art method. We also demonstrate our approach on real-world demand data from the ISO New England electricity market.

Authors who are presenting talks have a * after their name.

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