Artificial intelligence in renewable energy technologies and sustainable transition

Fareed, B, Vasić, L, Sher, F ORCID: https://orcid.org/0000-0003-2890-5912, Clauser, N, Soltani, M and Sofianos, MV, 2025. Artificial intelligence in renewable energy technologies and sustainable transition. In: Sher, F, ed., Renewable energy technologies. Elsevier, pp. 827-865. ISBN 9780443337710

Full text not available from this repository.

Abstract

An innovative method for maximizing energy production, consumption, and policy development is incorporating artificial intelligence (AI) in the renewable energy industry. By 2050, it is anticipated that the world's energy consumption will have increased by 50%, making AI-driven solutions crucial for improving sustainability, dependability, and efficiency. However, intermittent renewable energy sources like wind and solar present difficulties, and AI applications in natural language processing, machine learning, and predictive analytics provide encouraging answers. This research aims to increase the accuracy of energy forecasting, maximize grid dependability, and improve resource management in renewable energy systems. Large-scale datasets were analyzed, and real-world energy scenarios were simulated using AI-based defect detection systems, advanced neural networks, and anomaly detection algorithms. The findings show a 15–25% drop in energy waste, a 20–35% increase in energy forecasting accuracy, and a 30% reduction in operating expenses. Furthermore, compared with traditional techniques, AI-based grid optimization improves fault detection capabilities by 40% and energy distribution efficiency by 25%. This chapter's uniqueness is seen in its thorough AI-driven methodology that integrates real-time analytics for optimal decision-making across several renewable energy fields. Future uses include autonomous energy trading, AI-powered smart grids, and adaptive energy storage management, all of which have the potential to hasten the world's switch to sustainable energy. These results support a more robust and effective energy ecosystem by being in line with UN Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action).

Item Type:	Chapter in book
Creators:	Fareed, B., Vasić, L., Sher, F., Clauser, N., Soltani, M. and Sofianos, M.V.
Publisher:	Elsevier
Date:	2025
ISBN:	9780443337710
Identifiers:	Number Type 10.1016/b978-0-443-33771-0.00023-x DOI 2555954 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	06 Feb 2026 11:33
Last Modified:	06 Feb 2026 11:33
URI:	https://irep.ntu.ac.uk/id/eprint/55197

Actions (login required)

Edit View

Statistics

Views

Downloads