Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities
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It also involved using ML in empirical studies.
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From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [210]
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- Authors
- Vázquez-Canteli, J. R., Ulyanin, S., Kämpf, J., & Nagy, Z.
- Affiliations
- National Laboratory of the Rockies (US)
- Georgia Institute of Technology (US)
- Idiap Research Institute (CH)
- The University of Texas at Austin (US)
- Corresponding author
- Nagy, Zoltán
- Venue
- Sustainable Cities and Society
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 45
- Pages
- 243-257
- ISSN
- 2210-6707 (ISSN-L)
- OpenAlex
- W2900981450
- Cited by
- 195
- References
- 33
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 26 · 2023: 31 · 2022: 33 · 2021: 34 · 2020: 35 · 2019: 16 · 2018: 1
- FWCI
- 17.23
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Sustainable Cities and Society (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Keywords
- building energy simulation, TensorFlow, Keras, CitySim, deep reinforcement learning, building energy management, demand response, energy conservation, smart cities, building energy standards
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