Smart energy systems for sustainable smart cities: Current developments, trends and future directions

By E. O’Dwyer, Indranil Pan, Salvador Acha, Nilay Shah. Applied Energy, 2019

339 citations recorded by the source corpus.

Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/54e681724a94beac412d750c4bbffc344b9ef6fc

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Citations in the corpus

Artificial intelligence in business: State of the art and future research agenda (2020, background): “Such networked devices will generate significant amount of real-time behavioral data, which may only be analyzed using AI algorithms to uncover latent knowledge that can be used to help cities becoming better managed and more sustainable (O’Dwyer, 2019).”
Tackling Climate Change with Machine Learning (2019, methodology): “Low-emission infrastructure High Leverage When smart city projects are properly integrated into urban planning, they can make cities more sustainable and foster low-carbon lifestyles (see [310, 316, 317] for extensive reviews on this topic).”