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Artificial intelligence and energy storage

Artificial intelligence and energy storage

Author links open overlay panelRui Xiong a, Hailong Li b, Quanqing Yu c, Alessandro Romagnoli d, Jakub Jurasz e,https://doi.org/10.1016/j.egyai.2023.100268Get ri.

Artificial intelligence and energy storage

About Artificial intelligence and energy storage

Author links open overlay panelRui Xiong a, Hailong Li b, Quanqing Yu c, Alessandro Romagnoli d, Jakub Jurasz e,https://doi.org/10.1016/j.egyai.2023.100268Get ri.

The prompt development of renewable energies necessitates advanced energy storage.

The special issue on “Applications of AI in Advanced Energy Storage Technologies (AEST)” reports on recent applications of AI in the area of energy storage. A total of eight article.

Articles published in this special issue provide new insights into i) the design of driving cycles of vehicles; ii) seawater desalination with renewable energy; iii) the characteriz.

Prof.Rui Xiong acknowledges the support from the National Key R&D Program of China (2021YFB2402002) and the Beijing Natural Science Foundation (Grant No. L223013).

1.A. Chadzynski, N. Krdzavac, F. Farazi, M.Q. Lim, S. Li, A. Grisiute, P. Herthogs, A. von Richthofen, S. Cairns, M. KraftSemantic 3.

As the photovoltaic (PV) industry continues to evolve, advancements in Artificial intelligence and energy storage have become critical to optimizing the utilization of renewable energy sources. From innovative battery technologies to intelligent energy management systems, these solutions are transforming the way we store and distribute solar-generated electricity.

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6 FAQs about [Artificial intelligence and energy storage]

Can artificial intelligence improve advanced energy storage technologies (AEST)?

In this regard, artificial intelligence (AI) is a promising tool that provides new opportunities for advancing innovations in advanced energy storage technologies (AEST). Given this, Energy and AI organizes a special issue entitled “Applications of AI in Advanced Energy Storage Technologies (AEST)”.

How can AI optimize energy storage systems?

AI algorithms optimize energy storage systems (ESS) by forecasting energy production and consumption patterns. This allows for intelligent charging and discharging of batteries, maximizing their lifespan and efficiency. Additionally, AI can identify the most cost-effective times to store or release energy based on market prices.

Can AI improve energy storage based on physics?

In addition to these advances, emerging AI techniques such as deep neural networks [ 9, 10] and semisupervised learning are promising to spur innovations in the field of energy storage on the basis of our understanding of physics .

How a smart energy storage system can be developed?

Smart energy storage systems based on a high level of artificial intelligence can be developed. With the widespread use of the internet of things (IoT), especially their application in grid management and intelligent vehicles, the demand for the energy use efficiency and fast system response keeps growing.

Are intelligent systems able to store electricity?

Most recent publications in the energy field have been published in journals such as energy storage, advances in intelligent systems and chemical engineering journals. Based on this figure, we can conclude that intelligent systems with the ability to store electricity are being approached from different aspects. Fig. 10.

Can machine learning improve energy storage technology?

Besides the above-mentioned disciplines, machine learning technologies have great potentials for addressing the development and management of energy storage devices and systems by significantly improving the prediction accuracy and computational efficiency. Several recent reviews have highlighted the trend.

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