Abstract
Sorption thermal heat storage is a promising solution to improve the development of renewable energies and to promote a rational use of energy both for industry and households. These systems store thermal energy through physico-chemical sorption/desorption reactions that are also termed hydration/dehydration. Their introduction to the market requires to assess their energy performances, usually analysed by numerical simulation of the overall system. To address this, physical models are commonly developed and used. However, simulation based on such models are time-consuming which does not allow their use for yearly simulations. Artificial neural network (ANN)-based models, which are known for their computational efficiency, may overcome this issue. Therefore, the main objective of this study is to investigate the use of an ANN model to simulate a sorption heat storage system, instead of using a physical model. The neural network is trained using experimental results in order to evaluate this approach on actual systems. By using a recurrent neural network (RNN) and the Deep Learning Toolbox in MATLAB, a good accuracy is reached, and the predicted results are close to the experimental results. The root mean squared error for the prediction of the temperature difference during the thermal energy storage process is less than 3K for both hydration and dehydration, the maximal temperature difference being, respectively, about 90K and 40K.
Highlights
Renewable energy deployment is often applied in conjunction with Thermal EnergyStorage (TES) to balance the energy between production and demand, e.g., storing summer heat for winter heating
A smaller number of data go through one pass, and the recurrent neural network (RNN) will update itself more efficiently because it processes less data
An RNN model suited for zeolite-based thermal energy storage was constructed
Summary
Renewable energy deployment is often applied in conjunction with Thermal EnergyStorage (TES) to balance the energy between production and demand, e.g., storing summer heat for winter heating. Chemical and sorption TES have been identified as promising technologies to solve the seasonal mismatch of solar energy storage [1] offering high energy densities around 600 kW · h · m−3 and 200 kW · h · m−3 , respectively, [2]. Despite their high energy densities, chemical and sorption TES suffer from their low technology readiness level (typically two to three), which justifies the intensive research on this topic that has occurred in the last ten years [1,2,3,4,5,6,7,8].
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