D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 39 Citations 5,487 176 World Ranking 3963 National Ranking 1334

Overview

What is he best known for?

The fields of study he is best known for:

  • Management
  • Electrical engineering
  • Information technology

His primary areas of investigation include Demand response, Efficient energy use, Automation, Transport engineering and Energy management. The study incorporates disciplines such as Peak demand, Simulation, Electrical load and Air conditioning in addition to Demand response. His studies deal with areas such as Cost database, Benchmarking, End user and Energy as well as Efficient energy use.

His Automation research is multidisciplinary, incorporating elements of Environmental impact of the energy industry, Watson and Interoperability. His Transport engineering research is multidisciplinary, incorporating perspectives in Power management, Sectoral analysis and Electricity. His work carried out in the field of Electricity brings together such families of science as Energy consumption, Telecommunications equipment and Emerging technologies.

His most cited work include:

  • Quantifying Changes in Building Electricity Use, With Application to Demand Response (220 citations)
  • Electricity used by office equipment and network equipment in the U.S.: Detailed report and appendices (145 citations)
  • Analysis of an information monitoring and diagnostic system to improve building operations (122 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Demand response, Efficient energy use, Energy management, Electricity and Energy consumption. Mary Ann Piette studied Demand response and Renewable energy that intersect with Environmental economics. His Efficient energy use study combines topics from a wide range of disciplines, such as Energy conservation, Benchmarking, Control, HVAC and Architectural engineering.

The HVAC study which covers Automotive engineering that intersects with Energy. He focuses mostly in the field of Energy management, narrowing it down to matters related to Information system and, in some cases, Risk analysis. The Electricity study combines topics in areas such as Transport engineering and Smart grid.

He most often published in these fields:

  • Demand response (37.66%)
  • Efficient energy use (35.71%)
  • Energy management (18.18%)

What were the highlights of his more recent work (between 2017-2021)?

  • Efficient energy use (35.71%)
  • Demand response (37.66%)
  • Energy (10.39%)

In recent papers he was focusing on the following fields of study:

Mary Ann Piette focuses on Efficient energy use, Demand response, Energy, Environmental economics and Renewable energy. His research integrates issues of Energy conservation, Benchmarking, Reliability engineering, Interoperability and Analytics in his study of Efficient energy use. His work is dedicated to discovering how Benchmarking, CityGML are connected with Transport engineering and other disciplines.

His Transport engineering research incorporates themes from Electricity and Occupancy. His Energy study incorporates themes from Energy consumption, Ventilation, Natural resource economics and Civil engineering. He has included themes like Peak demand and Flexibility in his Renewable energy study.

Between 2017 and 2021, his most popular works were:

  • Building thermal load prediction through shallow machine learning and deep learning (33 citations)
  • Data fusion in predicting internal heat gains for office buildings through a deep learning approach (28 citations)
  • Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems (28 citations)

In his most recent research, the most cited papers focused on:

  • Management
  • Information technology
  • Electrical engineering

His scientific interests lie mostly in Model predictive control, Artificial intelligence, Energy, Building energy and Automotive engineering. Mary Ann Piette combines subjects such as Artificial neural network, HVAC and Building envelope with his study of Model predictive control. His Energy research integrates issues from Energy consumption, Risk analysis and Civil engineering.

His work carried out in the field of Building energy brings together such families of science as Control, Occupancy and Test set. His research investigates the link between Automotive engineering and topics such as Thermal that cross with problems in Efficient energy use. Mary Ann Piette performs integrative Efficient energy use and Upper and lower bounds research in his work.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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

J. L. Mathieu;P. N. Price;S. Kiliccote;M. A. Piette.
IEEE Transactions on Smart Grid (2011)

361 Citations

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.
Applied Energy (2017)

254 Citations

Electricity used by office equipment and network equipment in the US

Kaoru Kawamoto;Jonathan G Koomey;Bruce Nordman;Richard E Brown.
(2002)

240 Citations

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.
(2001)

222 Citations

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

Mary Ann Piette;Sat Kartar Kinney;Philip Haves.
Energy and Buildings (2001)

218 Citations

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

Rongxin Yin;Peng Xu;Mary Ann Piette;Sila Kiliccote.
Energy and Buildings (2010)

190 Citations

Commercial Building Energy Saver: An energy retrofit analysis toolkit

Tianzhen Hong;Mary Ann Piette;Yixing Chen;Sang Hoon Lee.
Applied Energy (2015)

156 Citations

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

Peng Xu;Philip Haves;Mary Ann Piette;James Braun.
Lawrence Berkeley National Laboratory (2004)

153 Citations

Statistical analysis of baseline load models for non-residential buildings

Katie Coughlin;Mary Ann Piette;Charles Goldman;Sila Kiliccote.
Energy and Buildings (2009)

141 Citations

Building thermal load prediction through shallow machine learning and deep learning

Zhe Wang;Tianzhen Hong;Mary Ann Piette.
Applied Energy (2020)

140 Citations

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