Next Article in Journal
Experimental Study of a Moored Floating Oscillating Water Column Wave-Energy Converter and of a Moored Cubic Box
Next Article in Special Issue
Forecasting in Blockchain-Based Local Energy Markets
Previous Article in Journal
Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs
Previous Article in Special Issue
Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits
Open AccessArticle

Intraday Load Forecasts with Uncertainty

1
Department of Applied Mathematics and Statistics, Colorado School Mines, Golden, CO 80401, USA
2
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; https://doi.org/10.3390/en12101833
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
Show Figures

Figure 1

MDPI and ACS Style

Kozak, D.; Holladay, S.; Fasshauer, G.E. Intraday Load Forecasts with Uncertainty. Energies 2019, 12, 1833.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop