Published In
Energies
Document Type
Article
Publication Date
2023
Subjects
Machine Learning, Artificial Intelligence, Electricity, Energy, Capacity, Ancillary Services, Reconstructability Analysis, Bayesian Networks, Support Vector Machines, Neural Networks
Abstract
This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This research accomplishes these aims. The models built in this paper identify wind forecast, sunrise/sunset and the hour of day as primary predictors of net load imbalance, among other variables, and show that the average size of the INC and DEC capacity requirements can be reduced by over 25% with the margin of error currently used in the industry while also significantly improving closeness and exceedance metrics. The reduction in INC and DEC capacity requirements would yield an approximate cost savings of $4 million annually for one of nineteen Western Energy Imbalance market participants. Reconstructability Analysis performs the best among the machine learning methods tested.
The related dataset is available here: Data From: Machine Learning Predictions of Electricity Capacity
Rights
2022 Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
DOI
10.3390/en16010187
Persistent Identifier
https://archives.pdx.edu/ds/psu/39069
Citation Details
Harris, M.; Kirby, E.; Agrawal, A.; Pokharel, R.; Puyleart, F.; Zwick, M. Machine Learning Predictions of Electricity Capacity. Energies 2023, 16, 187. https://doi.org/10.3390/en16010187