World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
81
Citations
21440
World Ranking
503
National Ranking
170

Overview

Tianzhen Hong is affiliated with the Lawrence Berkeley National Laboratory in the United States. Their research primarily spans the fields of Engineering and Environmental Science, with a particular focus on Building and Construction, Environmental Engineering, and Electrical and Electronic Engineering. This multidisciplinary expertise also extends into Renewable Energy, Sustainability, and the Environment, as well as Speech and Hearing subfields.

The scientist's work covers a diverse range of topics related to energy systems and building environments. These topics include Building Energy and Comfort Optimization, Wind and Air Flow Studies, Urban Heat Island Mitigation, Smart Grid Energy Management, Noise Effects and Management, Energy Load and Power Forecasting, and Energy Efficiency and Management.

Frequent publication venues for Tianzhen Hong include:

  • Building and Environment
  • Energy and Buildings
  • Advances in Applied Energy
  • Applied Energy
  • Building Simulation

Co-authors collaborating regularly with Tianzhen Hong feature:

  • Han Li
  • Kaiyu Sun
  • Zhe Wang
  • Wanni Zhang
  • Jeetika Malik

Recent papers authored or co-authored by Tianzhen Hong highlight contributions to the understanding of climate impacts on energy systems, machine learning applications in building control, and energy flexibility. Notable works include:

  • "Quantifying the impacts of climate change and extreme climate events on energy systems," 2020, Nature Energy
  • "Reinforcement learning for building controls: The opportunities and challenges," 2020, Applied Energy
  • "Building thermal load prediction through shallow machine learning and deep learning," 2020, Applied Energy
  • "Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications," 2021, Advances in Applied Energy
  • "State-of-the-art on research and applications of machine learning in the building life cycle," 2020, Energy and Buildings

Best Publications

  • Occupant behavior modeling for building performance simulation: Current state and future challenges:

    Da Yan;William O’Brien;Tianzhen Hong;Xiaohang Feng

  • Quantifying the impacts of climate change and extreme climate events on energy systems

    A. T.D. Perera;A. T.D. Perera;Vahid M. Nik;Vahid M. Nik;Vahid M. Nik;Deliang Chen;Jean Louis Scartezzini

  • Advances in research and applications of energy-related occupant behavior in buildings ☆

    Tianzhen Hong;Sarah C. Taylor-Lange;Simona D'Oca;Da Yan

  • Ten questions concerning occupant behavior in buildings: The big picture

    Tianzhen Hong;Da Yan;Simona D'Oca;Chien-fei Chen

  • Building simulation: an overview of developments and information sources

    Tianzhen Hong;S.K Chou;T.Y Bong

  • Ten questions on urban building energy modeling

    Tianzhen Hong;Yixing Chen;Yixing Chen;Xuan Luo;Na Luo

  • Reinforcement learning for building controls: The opportunities and challenges

    Zhe Wang;Tianzhen Hong

  • IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings

    Da Yan;Tianzhen Hong;Bing Dong;Ardeshir Mahdavi

  • IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods

    Hiroshi Yoshino;Tianzhen Hong;Natasa Nord

  • Building thermal load prediction through shallow machine learning and deep learning

    Zhe Wang;Tianzhen Hong;Mary Ann Piette

  • The human dimensions of energy use in buildings: A review

    Simona D’Oca;Tianzhen Hong;Jared Langevin

  • Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis

    Yixing Chen;Tianzhen Hong;Mary Ann Piette

  • An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework

    Tianzhen Hong;Simona D'Oca;Simona D'Oca;William J.N. Turner;William J.N. Turner;Sarah C. Taylor-Lange

  • Occupancy schedules learning process through a data mining framework

    Simona D’Oca;Tianzhen Hong

  • Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications

    Han Li;Zhe Wang;Tianzhen Hong;Mary Ann Piette

  • State-of-the-art on research and applications of machine learning in the building life cycle

    Tianzhen Hong;Zhe Wang;Xuan Luo;Wanni Zhang

  • A data-mining approach to discover patterns of window opening and closing behavior in offices

    Simona D'Oca;Tianzhen Hong

  • Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation

    William O'Brien;Andreas Wagner;Marcel Schweiker;Marcel Schweiker;Ardeshir Mahdavi

  • Simulation of occupancy in buildings

    Xiaohang Feng;Da Yan;Tianzhen Hong

  • Building simulation: Ten challenges

    Tianzhen Hong;Jared Langevin;Kaiyu Sun

  • A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures

    Kaiyu Sun;Tianzhen Hong

Frequent Co-Authors

Da Yan
Da Yan Tsinghua University
Mary Ann Piette
Mary Ann Piette Lawrence Berkeley National Laboratory
Xiaodong Xu
Xiaodong Xu University of Washington
Anna Laura Pisello
Anna Laura Pisello University of Perugia
William O'Brien
William O'Brien Carleton University
Bing Dong
Bing Dong Syracuse University
Borong Lin
Borong Lin Tsinghua University
Siaw Kiang Chou
Siaw Kiang Chou National University of Singapore
Michael Wetter
Michael Wetter Lawrence Berkeley National Laboratory
Costas J. Spanos
Costas J. Spanos University of California, Berkeley

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