Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/22936
Title: Review of application of AI techniques to Solar Tower Systems
Authors: Milidonis, Kypros 
Blanco, Manuel J. 
Grigoriev, Victor 
Panagiotou, Constantinos F. 
Bonanos, Aristides M. 
Constantinou, Marios 
Pye, John 
Asselineau, Charles Alexis 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Concentrating solar thermal;Solar towers;Central receiver systems;Artificial intelligence;Optimization;Metaheuristics;Artificial neural networks
Issue Date: Aug-2021
Source: Solar Energy, 2021, vol. 224, pp. 500-515
Volume: 224
Start page: 500
End page: 515
Journal: Solar Energy 
Abstract: Artificial Intelligence (AI) is increasingly playing a significant role in the design and optimization of renewable energy systems. Many AI approaches and technologies are already widely deployed in the energy sector in applications such as generation forecasting, energy efficiency monitoring, energy storage, and overall design of energy systems. This paper provides a review of the applications of key AI techniques on the analysis, design, optimization, control, operation, and maintenance of Solar Tower systems, one of the most important types of Concentrating Solar Thermal (CST) systems. First, key AI techniques are briefly described and relevant examples of their application to CST systems in general are provided. Subsequently, a detailed review of how these AI techniques are being used to advance the state of the art of solar tower systems is presented. The review is structured around the different subsystems of a solar tower system.
URI: https://hdl.handle.net/20.500.14279/22936
ISSN: 0038092X
DOI: 10.1016/j.solener.2021.06.009
Rights: © Elsevier
Type: Article
Affiliation : The Cyprus Institute 
Australian National University 
Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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