Article
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Intraday Load Forecasts with Uncertainty
Version 1
: Received: 3 April 2019 / Approved: 4 April 2019 / Online: 4 April 2019 (16:01:54 CEST)
A peer-reviewed article of this Preprint also exists.
Kozak, D.; Holladay, S.; Fasshauer, G.E. Intraday Load Forecasts with Uncertainty. Energies 2019, 12, 1833. Kozak, D.; Holladay, S.; Fasshauer, G.E. Intraday Load Forecasts with Uncertainty. Energies 2019, 12, 1833.
Abstract
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 PJMISO 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 hours.
Keywords
load forecast; short term; probabilistic; Gaussian processes
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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