Introduction

Thermal energy storage plays a key role in energy efficiency and sustainability. Advances in AI (Artificial Intelligence) technologies are enabling the development of new and efficient ways to store thermal energy. In this paper, we present the existing methods for storing thermal energy using AI.

1. Predictive control

Predictive control uses AI algorithms to optimize the storage of thermal energy. The algorithms analyze system data and can predict thermal energy demand. This allows the storage system to operate more efficiently and optimise the use of thermal energy.

2. Neural networks

Neural networks are AI techniques that can learn and adapt to environmental changes. When applied to thermal energy storage, neural networks are able to optimise the operation of the storage system based on different temperature and time variations. This increases the efficiency of the storage system and the efficiency of thermal energy use.

3. Genetic algorithms

Genetic algorithms are AI techniques that apply evolutionary principles to problem solving. When applied to thermal energy storage, genetic algorithms are able to optimise the parameters of the storage system to achieve the best result. In this way, the efficiency of the storage system and the efficiency of thermal energy use can be improved.

4. Data mining

Data mining is the application of AI techniques to analyse large data sets and extract information. When applied to thermal energy storage, data mining enables the analysis of system data and the identification of patterns and correlations in thermal energy use. This can improve the efficiency of the storage system and the effectiveness of thermal energy use.

5. Machine learning

Machine learning is AI techniques that can learn from experience and adapt to a changing environment. When applied to thermal energy storage, machine learning allows the system to be optimised for thermal energy use efficiency. Algorithms can identify the best storage methods and parameters based on system data.

Summary

AI technologies offer a number of new and efficient ways to store thermal energy. Predictive control, neural networks, genetic algorithms, data mining and machine learning can all contribute to optimizing the efficiency of storage systems and the use of thermal energy. AI can help make thermal energy storage more efficient and sustainable, which can contribute to energy efficiency and sustainability.

∑: energy, storage, system, efficiency, algorithms, neural, networks, techniques, applied