Study on Auto-DR and Pre-Cooling of Commercial Buildings with Thermal Mass in California

[1]  A. Richards.  Energy and buildings , 2012 .

[2]  Sila Kiliccote,et al.  Open Automated Demand Response Communications Specification (Version 1.0) , 2009 .

[3]  James E. Braun,et al.  Evaluation of methods for determining demand-limiting setpoint trajectories in buildings using short-term measurements , 2008 .

[4]  J. Braun,et al.  Model-based demand-limiting control of building thermal mass , 2008 .

[5]  Sila Kiliccote,et al.  Design and Implementation of an Open, Interoperable Automated Demand Response Infrastructure , 2008 .

[6]  P. Torcellini,et al.  DOE Commercial Building Benchmark Models , 2008 .

[7]  Sila Kiliccote,et al.  Estimating Demand Response Load Impacts: Evaluation of BaselineLoad Models for Non-Residential Buildings in California , 2008 .

[8]  Sila Kiliccote,et al.  Design and Implementation of an Open, Interoperable AutomatedDemand Response Infrastructure , 2007 .

[9]  Gang Wu,et al.  Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai , 2007 .

[10]  Peng Xu,et al.  Introduction to Commercial Building Control Strategies and Techniques for Demand Response , 2007 .

[11]  T. Agami Reddy,et al.  Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part I: General Methodology (RP-1051) , 2007 .

[12]  T. Agami Reddy,et al.  Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part II: Application to Three Case Study Office Buildings (RP-1051) , 2007 .

[13]  Kyoung-ho Leea,et al.  Development of methods for determining demand-limiting setpoint trajectories in buildings using short-term measurements , 2007 .

[14]  Peng Xu,et al.  Case Study of Demand Shifting with Thermal Mass in Two Large Commercial Buildings , 2006 .

[15]  Philip Haves,et al.  Demand Shifting With Thermal Mass in Large Commercial Buildings: Field Tests, Simulation and Audits , 2005 .

[16]  Steven G. Johnson,et al.  The Design and Implementation of FFTW3 , 2005, Proceedings of the IEEE.

[17]  M. Piette,et al.  Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building , 2004 .

[18]  Development and evaluation of fully automated demand response in large facilities , 2004 .

[19]  David E. Claridge,et al.  Calibration Procedure for Energy Performance Simulation of a Commercial Building , 2003 .

[20]  J. Braun,et al.  Load Control Using Building Thermal Mass , 2003 .

[21]  Jeff Haberl,et al.  The great energy predictor shootout. II : Measuring retrofit savings , 1998 .

[22]  James E. Braun,et al.  Application of building precooling to reduce peak cooling requirements , 1997 .

[23]  J. F. Kreider.  Prediction Hourly Building Energy Use : The Great Energy Predictor Shootout - Overview and Discussion of Results , 1994 .

[24]  Jeff Haberl,et al.  Predicting hourly building energy usage , 1994 .

[25]  J. Braun,et al.  Experimental and simulated performance of optimal control of building thermal storage , 1994 .

[26]  L. K. Norford,et al.  Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: insights from a reconciliation based on the DOE-2 model , 1994 .

[27]  L. K. Norford,et al.  Peak load reduction by preconditioning buildings at night , 1991 .

[28]  H. Akbari,et al.  481 Prototypical commercial buildings for 20 urban market areas , 1991 .