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 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.
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.
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.
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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)
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)
Electricity used by office equipment and network equipment in the US
Kaoru Kawamoto;Jonathan G Koomey;Bruce Nordman;Richard E Brown.
(2002)
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)
Analysis of an information monitoring and diagnostic system to improve building operations
Mary Ann Piette;Sat Kartar Kinney;Philip Haves.
Energy and Buildings (2001)
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)
Commercial Building Energy Saver: An energy retrofit analysis toolkit
Tianzhen Hong;Mary Ann Piette;Yixing Chen;Sang Hoon Lee.
Applied Energy (2015)
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)
Statistical analysis of baseline load models for non-residential buildings
Katie Coughlin;Mary Ann Piette;Charles Goldman;Sila Kiliccote.
Energy and Buildings (2009)
Building thermal load prediction through shallow machine learning and deep learning
Zhe Wang;Tianzhen Hong;Mary Ann Piette.
Applied Energy (2020)
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