Spatial prediction of thermal power storage field

Can artificial intelligence be used for Intelligent Thermal energy storage?

Artificial intelligence (AI) is vital for intelligent thermal energy storage (TES). AI applications in modelling, design and control of the TES are summarized. A general strategy of the completely AI-based design and control of TES is presented. Research on the AI-integrated TES should match the feature of future energy system.

What is thermal energy storage?

Since thermal energy storage (TES) possesses the capability to temporarily store and reallocate the thermal energy, it has been widely employed in various fields. TES opens up an important avenue to the promotion of renewable energy utilization and energy saving.

Is thermal energy storage a viable alternative to AI-integrated TES?

The insufficiency of TES database hinders the practise of the AI-integrated TES. Capable of storing and redistributing energy, thermal energy storage (TES) shows a promising applicability in energy systems.

Can AI predict thermo-chemical energy storage performance?

Compared with STES and LTES, investigations on the performance prediction of thermo-chemical energy storage (TCES) using AI methods are rather limited.

How is time/space separation used to decompose spatio-temporal thermal dynamics?

In this method, the time/space (T/S) separation is adopted to decompose the spatio-temporal thermal dynamics. Under the T/S separation, an incremental-learning-based regulator is first employed for the recursive update of spatial basis functions, which can represent the most recent spatial complexity.

What is correlation-based long-term memory (C-LSTM) for distributed electric heating TES?

Wang et al. developed a prediction model with correlation-based long short-term memory (C-LSTM) for distributed electric heating TES in the CHP networks. The prediction model identified the characteristics of the distributed TES with external factors, laying a foundation for the electricity and heat dispatch optimization.