Machine Learning Predictions of Electricity Capacity

Marcus Harris (), Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart and Martin Zwick
Additional contact information
Marcus Harris: Systems Science Program, Portland State University, Portland, OR 97201, USA
Elizabeth Kirby: Bonneville Power Administration, Portland, OR 97232, USA
Ameeta Agrawal: Computer Science Department, Portland State University, Portland, OR 97201, USA
Rhitabrat Pokharel: Computer Science Department, Portland State University, Portland, OR 97201, USA
Francis Puyleart: Bonneville Power Administration, Portland, OR 97232, USA
Martin Zwick: Systems Science Program, Portland State University, Portland, OR 97201, USA

Energies, 2022, vol. 16, issue 1, 1-29

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.

Keywords: machine learning; artificial intelligence; electricity; energy; capacity; ancillary services; reconstructability analysis; Bayesian Networks; support vector machines; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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