World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
52
Citations
8585
World Ranking
3696
National Ranking
1074

Overview

Mary Ann Piette is affiliated with Lawrence Berkeley National Laboratory in the United States, focusing on research within the field of engineering. Their work spans several subfields including electrical and electronic engineering, building and construction, environmental engineering, renewable energy, sustainability and the environment, and computer science applications.

Their research primarily covers topics such as building energy and comfort optimization, smart grid energy management, energy efficiency and management, wind and air flow studies, energy load and power forecasting, integrated energy systems optimization, and urban heat island mitigation.

Frequent publication venues for Mary Ann Piette include OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Advances in Applied Energy, Applied Energy, Building and Environment, and Energies.

Notable recent papers include:

  • 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
  • Field demonstration and implementation analysis of model predictive control in an office HVAC system, 2022, Applied Energy
  • Predicting city-scale daily electricity consumption using data-driven models, 2021, Advances in Applied Energy
  • A three-year dataset supporting research on building energy management and occupancy analytics, 2022, Scientific Data

Mary Ann Piette has collaborated frequently with several co-authors, including Marco Pritoni, Tianzhen Hong, Rongxin Yin, Jingjing Liu, and Armando Casillas. These collaborations reflect ongoing research efforts in related fields.

Best Publications

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

    Zhe Wang;Tianzhen Hong;Mary Ann Piette

  • Quantifying Changes in Building Electricity Use, With Application to Demand Response

    J. L. Mathieu;P. N. Price;S. Kiliccote;M. A. Piette

  • 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

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

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

  • Electricity used by office equipment and network equipment in the US

    Kaoru Kawamoto;Jonathan G Koomey;Bruce Nordman;Richard E Brown

  • Analysis of an information monitoring and diagnostic system to improve building operations

    Mary Ann Piette;Sat Kartar Kinney;Philip Haves

  • Study on Auto-DR and pre-cooling of commercial buildings with thermal mass in California

    Rongxin Yin;Peng Xu;Mary Ann Piette;Sila Kiliccote

  • Electricity used by office equipment and network equipment in the U.S.: Detailed report and appendices

    Kaoru Kawamoto;Jonathan G. Koomey;Bruce Nordman;Richard E. Brown

  • Field demonstration and implementation analysis of model predictive control in an office HVAC system

    Unknown

  • Commercial Building Energy Saver: An energy retrofit analysis toolkit

    Tianzhen Hong;Mary Ann Piette;Yixing Chen;Sang Hoon Lee

  • Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building

    Peng Xu;Philip Haves;Mary Ann Piette;James Braun

  • Statistical analysis of baseline load models for non-residential buildings

    Katie Coughlin;Mary Ann Piette;Charles Goldman;Sila Kiliccote

  • Energy retrofit analysis toolkits for commercial buildings: A review

    Sang Hoon Lee;Tianzhen Hong;Mary Ann Piette;Sarah C. Taylor-Lange

  • Development and evaluation of fully automated demand response in large facilities

    Mary Ann Piette;Osman Sezgen;David S. Watson;Naoya Motegi

  • Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques

    Marco Bonvini;Michael D. Sohn;Jessica Granderson;Michael Wetter

  • Estimating Demand Response Load Impacts: Evaluation of BaselineLoad Models for Non-Residential Buildings in California

    Katie Coughlin;Mary Ann Piette;Charles Goldman;Sila Kiliccote

  • Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems

    D.H. Blum;K. Arendt;L. Rivalin;M.A. Piette

  • Open Automated Demand Response Communications Specification (Version 1.0)

    Mary Ann Piette;Girish Ghatikar;Sila Kiliccote;Ed Koch

  • A pattern-based automated approach to building energy model calibration

    Kaiyu Sun;Tianzhen Hong;Sarah C. Taylor-Lange;Mary Ann Piette

  • Building energy information systems: user case studies

    Jessica Granderson;Mary Ann Piette;Girish Ghatikar

  • Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings

    Mary Ann Piette;Girish Ghatikar;Sila Kiliccote;David Watson

  • Solutions for Summer Electric Power Shortages: Demand Response and its Applications in Air Conditioning and Refrigerating Systems

    Junqiao Han;Mary Ann Piette

Frequent Co-Authors

Tianzhen Hong
Tianzhen Hong Lawrence Berkeley National Laboratory
Michael Wetter
Michael Wetter Lawrence Berkeley National Laboratory
Alan Meier
Alan Meier Lawrence Berkeley National Laboratory
Johanna L. Mathieu
Johanna L. Mathieu University of Michigan–Ann Arbor
David E. Claridge
David E. Claridge Texas A&M University
Adam Z. Weber
Adam Z. Weber Lawrence Berkeley National Laboratory
Patrick E. Phelan
Patrick E. Phelan Arizona State University
Peter Palensky
Peter Palensky Delft University of Technology
Hans-Arno Jacobsen
Hans-Arno Jacobsen University of Toronto

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Best Scientists Citing Mary Ann Piette

Trending Scientists