Discrete-time state-of-charge estimator for latent heat thermal energy storage units based on a recurrent neural network

Bastida Hernandez, Jose, De la Cruz Loredo, Ivan, Saikia, Pranaynil and Ugalde Loo, Carlos ORCID: https://orcid.org/0000-0001-6361-4454 2024. Discrete-time state-of-charge estimator for latent heat thermal energy storage units based on a recurrent neural network. Applied Energy 371 , 123526. 10.1016/j.apenergy.2024.123526

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Abstract

Energy storage systems enable balancing supply and demand and facilitate the integration of intermittent renewable energy sources. In particular, latent heat thermal energy storage units are attractive for deployment in thermal systems due to their high energy density. However, knowledge of the state-of-charge of a thermal store is crucial to effectively regulate its charging and discharging cycles. To achieve this, continuous-time non-linear observers may be employed to estimate the state-of-charge at the expense of a high computational cost. The emergence of artificial intelligence solutions may be helpful to reduce computational burden, but their adoption for thermal stores has been limited. This paper bridges this research gap by presenting a novel approach to predict the state-of-charge of a latent heat unit. It employs a discrete-time estimator based on a recurrent neural network, which is based on a long short-term memory structure and the regression method for estimation. The estimator offers a reduced computation time for state-of-charge estimation and allows flexible sampling adjustments without sacrificing accuracy. Additionally, the presented approach simplifies data collection by independently handling charging and discharging processes through internal state resets. The estimator, trained using MATLAB’s deep learning toolbox, uses a dataset comprising various operating conditions obtained from simulations of a physics-based model. When compared against a more traditional state-of-charge estimation method using a discrete-time non-linear observer, the advantages of a recurrent neural network-based estimator are evidenced, highlighting its potential for practical applications. The presented method exhibited high accuracy with a maximum root mean square error under 0.73% and a mean absolute error below 0.41% with respect to direct state-of-charge calculation. Although the discrete-time non-linear observer exhibited a marginal higher accuracy, the recurrent neural network-based estimator achieved a significant improvement in computational efficiency. These findings make the proposed approach a robust tool facilitating enhanced control strategies, optimised energy management, and increased overall thermal system performance.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	1068-7181
Funders:	EPRSC
Date of First Compliant Deposit:	13 June 2024
Date of Acceptance:	20 May 2024
Last Modified:	13 Jun 2024 10:16
URI:	https://orca.cardiff.ac.uk/id/eprint/169132

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