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Development of a NARX state-of-charge predictor based on active power demand

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1109/GridEdge54130.2023.10102751
AuthorSearch for: 1; Search for: ; Search for: 1; Search for: 2
Affiliation
  1. National Research Council of Canada. Aerospace
  2. National Research Council of Canada. Energy, Mining and Environment
FunderSearch for: Research and Development
FormatText, Article
Conference2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge), April 10-13, 2023, San Diego, California, United States
Subjectenergy storage system; machine learning; modelling; NARX; neural network; state-of-charge prediction
Abstract
A simple neural network state-of-charge predictor trained on one-year of energy storage system data is presented. The model uses the active power command and the state-of-charge for the current time-step, and implements a nonlinear auto-regressive network with exogenous inputs to predict the state-of-charge at the subsequent time-step. The neural network training algorithm is written in the Julia programming language, independent of any existing machine learning platforms; the resulting model is compared to one developed using Python/TensorFlow. The simulation performance was validated with data collected from the energy storage system that was dispatched to follow a standard frequency regulation duty cycle not used as part of the training data. The mean-absolute-error between the predicted state of charge and the validation data is shown to be less then 1%, despite the limited data and lack of physical information about the system.
Publication date
PublisherIEEE
In
  • 2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge), 10102751 (18 April 2023): 1–5.
LanguageEnglish
Peer reviewedYes
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Record identifierc65ef39d-c353-416c-b532-5b201c13aa40
Record created2023-06-23
Record modified2023-06-23
Date modified: