期刊
APPLIED ENERGY
卷 237, 期 -, 页码 581-597出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2019.01.024
关键词
Smart cities; Computational intelligence; Machine learning; Predictive control; Smart energy systems
资金
- European Union's Horizon 2020 research and innovation programme [691895]
- Imperial College Research Fellowship scheme
- H2020 Societal Challenges Programme [691895] Funding Source: H2020 Societal Challenges Programme
- EPSRC [EP/S016627/1] Funding Source: UKRI
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.
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