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Open AccessArticle

Intraday Load Forecasts with Uncertainty

Department of Applied Mathematics and Statistics, Colorado School Mines, Golden, CO 80401, USA
Department of Economics, Haslam School of Business, University of Tennessee, Knoxville, TN 37916, USA
Author to whom correspondence should be addressed.
Energies 2019, 12(10), 1833;
Received: 3 April 2019 / Revised: 5 May 2019 / Accepted: 8 May 2019 / Published: 14 May 2019
(This article belongs to the Special Issue Modeling and Forecasting Intraday Electricity Markets)
We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the largest deregulated wholesale U.S. electricity market for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five minute forecasts for 24 h. View Full-Text
Keywords: load forecast; short term; probabilistic; Gaussian processes load forecast; short term; probabilistic; Gaussian processes
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Kozak, D.; Holladay, S.; Fasshauer, G.E. Intraday Load Forecasts with Uncertainty. Energies 2019, 12, 1833.

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