Intelligent data systems for building energy workflow: Data pipelines, LSTM efficiency prediction and more; Chandra Y. Matuška T. (2022)

Energy and Buildings (2022). https://doi.org/10.1016/j.enbuild.2022.112135

The data collection process for thermal energy storage (TES) system is largely still and restricted to data collection only. This leaves a gap to study the transient state physical process of charge and discharge as it proceeds. In addition, these devices are restricted and cannot perform on spot model fitting, prediction and other data curation techniques. This paper demonstrates the application of intelligent data layer with neural networks for evaluating and predicting end to end performance of heat pump integrated stratified thermal energy storage (TES) system.

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