Intelligent system for lighting control in smart cities
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ML and AI can help coordinate district heating and cooling networks, solar power generation, and charging stations for EVs and bikes, and can improve public lighting systems by regulating light intensity based on historical patterns of foot traffic.
“ML and AI can help, for example, to coordinate district heating and cooling networks, solar power generation, and charging stations for EVs and bikes [602], and can improve public lighting systems by regulating light intensity based on historical patterns of foot traffic [169].”
From Tackling climate change with machine learning (2023) · cites this paper as [169]
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- Authors
- De Paz, J. F., Bajo, J., Rodríguez, S., Villarrubia, G., & Corchado, J. M.
- Affiliations
- Universidad Politécnica de Madrid (ES)
- Universidad de Salamanca (ES)
- Corresponding author
- Bajo, Javier
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 372
- Pages
- 241-255
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2507582667
- Cited by
- 146
- References
- 28
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 10 · 2023: 18 · 2022: 17 · 2021: 18 · 2020: 22 · 2019: 13 · 2018: 26 · 2017: 8 · 2016: 1
- FWCI
- 11.87
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Information Sciences (publishedVersion)
- Gredos (University of Salamanca) (submittedVersion, cc-by-nc-nd)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- Hispana (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Impact of Light on Environment and Health
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Keywords
- smart cities, energy optimization, visual comfort, artificial neural networks, multi-agent systems, expectation-maximization algorithm, analysis of variance, service-oriented architecture, adaptive architecture, lighting cost reduction
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