Mary Ann Piette

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Sila KiliccoteLBNL, SLAC, Google, eIQ Mobility, Breakthrough Energy VenturesEmail verificata su kiliccote.com
Tianzhen HongSenior Scientist, FIBPSA, FASHRAE, Lawrence Berkeley National LabEmail verificata su lbl.gov
Bruce NordmanResearch Scientist, Lawrence Berkeley National LaboratoryEmail verificata su lbl.gov
Rongxin YinUC Berkeley/Lawrence Berkeley National LabEmail verificata su lbl.gov
Philip HavesLawrence Berkeley National LaboratoryEmail verificata su lbl.gov
Richard BrownLawrence Berkeley National LaboratoryEmail verificata su lbl.gov
Michael WetterLBNL, Berkeley, CAEmail verificata su lbl.gov
Sang Hoon LeeSenior Scientific Engineering Associate, Lawrence Berkeley National LaboratoryEmail verificata su lbl.gov
Evan MillsSenior Scientist (retired)Email verificata su lbl.gov
Michael D. SohnLawrence Berkeley National LaboratoryEmail verificata su lbl.gov
Sarah C. Taylor-LangeLawrence Berkeley National LaboratoryEmail verificata su lbl.gov
Jonathan KoomeyPresident, Koomey AnalyticsEmail verificata su koomey.com
Kaoru KawamotoProfessor, Data Science División, Shiga University, JapanEmail verificata su osakagas.co.jp
Stephen SelkowitzLawrence Berkeley National LabEmail verificata su lbl.gov
Johanna MathieuAssociate Professor of Electrical Engineering and Computer Science, University of MichiganEmail verificata su umich.edu
David E. ClaridgeTexas A&M UniversityEmail verificata su tamu.edu
Steven LanziseraLawrence Berkeley National LaboratoryEmail verificata su lbl.gov
Frank OlkenFrank Olken Consulting

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Mary Ann Piette

LBNL

Email verificata su lbl.gov


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Quantifying changes in building electricity use, with application to demand response JL Mathieu, PN Price, S Kiliccote, MA Piette IEEE Transactions on Smart Grid 2 (3), 507-518, 2011	437	2011
Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis Y Chen, T Hong, MA Piette Applied Energy 205, 323-335, 2017	427	2017
Building thermal load prediction through shallow machine learning and deep learning Z Wang, T Hong, MA Piette Applied Energy 263, 114683, 2020	397	2020
Introduction to Commercial Building Control Strategies and Techniques for Demand Response--Appendices N Motegi	313	2007
Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications H Li, Z Wang, T Hong, MA Piette Advances in Applied Energy 3, 100054, 2021	312	2021
Analysis of an information monitoring and diagnostic system to improve building operations MA Piette, SK Kinney, P Haves Energy and Buildings 33 (8), 783-791, 2001	278	2001
Electricity used by office equipment and network equipment in the US K Kawamoto, JG Koomey, B Nordman, RE Brown, MA Piette, M Ting, ... Energy 27 (3), 255-269, 2002	266*	2002
Study on Auto-DR and pre-cooling of commercial buildings with thermal mass in California R Yin, P Xu, MA Piette, S Kiliccote Energy and Buildings 42 (7), 967-975, 2010	240	2010
Commercial building energy saver: An energy retrofit analysis toolkit T Hong, MA Piette, Y Chen, SH Lee, SC Taylor-Lange, R Zhang, K Sun, ... Applied Energy 159, 298-309, 2015	220	2015
CityBES: A web-based platform to support city-scale building energy efficiency T Hong, Y Chen, SH Lee, MA Piette Urban Computing 14, 2016, 2016	193	2016
Peak demand reduction from pre-cooling with zone temperature reset in an office building P Xu, P Haves, MA Piette, J Braun	185	2004
Statistical analysis of baseline load models for non-residential buildings K Coughlin, MA Piette, C Goldman, S Kiliccote Energy and Buildings 41 (4), 374-381, 2009	167	2009
The cost-effectiveness of commercial-buildings commissioning E Mills, H Friedman, T Powell, N Bourassa, D Claridge, T Haasl, MA Piette LBNL-56637, 2004	167	2004
Energy retrofit analysis toolkits for commercial buildings: A review SH Lee, T Hong, MA Piette, SC Taylor-Lange Energy 89, 1087-1100, 2015	165	2015
Demand response opportunities and enabling technologies for data centers: Findings from field studies G Ghatikar	160	2012
Machine to machine (M2M) technology in demand responsive commercial buildings DS Watson, MA Piette, O Sezgen, N Motegi Handbook of Web Based Energy Information and Control Systems, 429-446, 2020	157	2020
Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques M Bonvini, MD Sohn, J Granderson, M Wetter, MA Piette Applied Energy 124, 156-166, 2014	149	2014
Estimating demand response load impacts: Evaluation of baseline load models for non-residential buildings in California K Coughlin, MA Piette, C Goldman, S Kiliccote	142	2008
Open automated demand response communications specification (Version 1.0) MA Piette	133	2009
Development and evaluation of fully automated demand response in large facilities MA Piette, O Sezgen, DS Watson, N Motegi, C Shockman, L Ten Hope	132	2004

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