Abstract
Modelling of thermal energy storage (TES) systems is a complex process that requires the development of sophisticated computational tools for numerical simulation and optimization. Until recently, most modelling approaches relied on analytical methods based on equations of the physical processes that govern TES systems’ operations, producing high-accuracy and interpretable results. The present study tackles the problem of modelling the temperature dynamics of a TES plant by exploring the advantages and limitations of an alternative data-driven approach. A hybrid bimodal LSTM (H2M-LSTM) architecture is proposed to model the temperature dynamics of different TES components, by utilizing multiple temperature readings in both forward and bidirectional fashion for fine-tuning the predictions. Initially, a selection of methods was employed to model the temperature dynamics of individual components of the TES system. Subsequently, a novel cascading modelling framework was realised to provide an integrated holistic modelling solution that takes into account the results of the individual modelling components. The cascading framework was built in a hierarchical structure that considers the interrelationships between the integrated energy components leading to seamless modelling of whole operation as a single system. The performance of the proposed H2M-LSTM was compared against a variety of well-known machine learning algorithms through an extensive experimental analysis. The efficacy of the proposed energy framework was demonstrated in comparison to the modelling performance of the individual components, by utilizing three prediction performance indicators. The findings of the present study offer: (i) insights on the low-error performance of tailor-made LSTM architectures fitting the TES modelling problem, (ii) deeper knowledge of the behaviour of integral energy frameworks operating in fine timescales and (iii) an alternative approach that enables the real-time or semi-real time deployment of TES modelling tools facilitating their use in real-world settings.
Highlights
IntroductionModelling and optimization play a crucial role towards the effective management of such multi-vector energy systems
The second source is a cluster of biomass boilers that operate on wood chips and oil, as well as a combined heat and power (CHP) plant that operates on wood chips
Analytical models have already proven their performance in modelling thermal energy storage (TES) plants
Summary
Modelling and optimization play a crucial role towards the effective management of such multi-vector energy systems. Several attempts have been reported in the recent literature using physics-based approaches for the modelling of individual DHC components [1]. Most of these studies decompose complex energy systems into a series of simpler input-output energy hubs [2]. Pivotal to the optimization of a decentralized DHC network is the adoption of holistic management methodologies that take into account all aspects of the system. To optimize today’s multi-vector energy systems, accurate internal models of all the primary and secondary energy sources need to be produced
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