<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Simulation Approach to Estimate Energy Savings Potential of Occupant Behavior Measures</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">behavior measure</style></keyword><keyword><style  face="normal" font="default" size="100%">Behavior Modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">building performance simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy savings</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">occupant behavior</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Occupant behavior in buildings is a leading factor influencing energy use in buildings. Low-cost behavioral solutions have demonstrated significant potential energy savings. Estimating the behavioral savings potential is important for a more effective design of behavior change interventions, which in turn will support more effective energy-efficiency policies. This study introduces a simulation approach to estimate the energy savings potential of occupant behavior measures. First it defines five typical occupant behavior measures in office buildings, then simulates and analyzes their individual and integrated impact on energy use in buildings. The energy performance of the five behavior measures was evaluated using EnergyPlus simulation for a real office building across four typical U.S. climates and two vintages. The Occupancy Simulator was used to simulate the occupant movement in each zone with inputs from the site survey of the case building. Based on the simulation results, the occupant behavior measures can achieve overall site energy savings as high as 22.9% for individual measures and up to 41.0% for integrated measures. Although energy savings of behavior measures would vary depending upon many factors, the presented simulation approach is robust and can be adopted for other studies aiming to quantify occupant behavior impact on building performance.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author><author><style face="normal" font="default" size="100%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">Sang Hoon Lee</style></author><author><style face="normal" font="default" size="100%">Sarah C. Taylor-Lange</style></author><author><style face="normal" font="default" size="100%">Rongpeng Zhang</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Phillip N. Price</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Commercial Building Energy Saver: An energy retrofit analysis toolkit</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Energy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Building Technologies Department</style></keyword><keyword><style  face="normal" font="default" size="100%">Building Technology and Urban Systems Division</style></keyword><keyword><style  face="normal" font="default" size="100%">buildings</style></keyword><keyword><style  face="normal" font="default" size="100%">buildings energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">Commercial Building Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">conservation measures</style></keyword><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">energy use</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">External</style></keyword><keyword><style  face="normal" font="default" size="100%">Retrofit Energy</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation research</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">9/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">159</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Small commercial buildings in the United States consume 47% of the total primary energy of the buildings sector. Retrofitting small and medium commercial buildings poses a huge challenge for owners because they usually lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper presents the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit, which calculates the energy use of a building, identifies and evaluates retrofit measures in terms of energy savings, energy cost savings and payback. The CBES Toolkit includes a web app (APP) for end users and the CBES Application Programming Interface (API) for integrating CBES with other energy software tools. The toolkit provides a rich set of features including: (1) Energy Benchmarking providing an Energy Star score, (2) Load Shape Analysis to identify potential building operation improvements, (3) Preliminary Retrofit Analysis which uses a custom developed pre-simulated database and, (4) Detailed Retrofit Analysis which utilizes real-time EnergyPlus simulations. CBES includes 100 configurable energy conservation measures (ECMs) that encompass IAQ, technical performance and cost data, for assessing 7 different prototype buildings in 16 climate zones in California and 6 vintages. A case study of a small office building demonstrates the use of the toolkit for retrofit analysis. The development of CBES provides a new contribution to the field by providing a straightforward and uncomplicated decision making process for small and medium business owners, leveraging different levels of assessment dependent upon user background, preference and data availability.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004502</style></custom2><section><style face="normal" font="default" size="100%">298</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Kaiyu Sun</style></author><author><style face="normal" font="default" size="100%">Rongpeng Zhang</style></author><author><style face="normal" font="default" size="100%">Ryohei Hinokuma</style></author><author><style face="normal" font="default" size="100%">Shinichi Kasahara</style></author><author><style face="normal" font="default" size="100%">Yoshinori Yura</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development and validation of a new variable refrigerant flow systemmodel in EnergyPlus</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">building simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">energy modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">energyplus</style></keyword><keyword><style  face="normal" font="default" size="100%">Heat pump</style></keyword><keyword><style  face="normal" font="default" size="100%">model validation</style></keyword><keyword><style  face="normal" font="default" size="100%">Variable refrigerant flow</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">9/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">117</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Variable refrigerant flow (VRF) systems vary the refrigerant flow to meet the dynamic zone thermalloads, leading to more efficient operations than other system types. This paper introduces a new modelthat simulates the energy performance of VRF systems in the heat pump (HP) operation mode. Com-pared with the current VRF-HP models implemented in EnergyPlus, the new VRF system model has morecomponent models based on physics and thus has significant innovations in: (1) enabling advanced con-trols, including variable evaporating and condensing temperatures in the indoor and outdoor units, andvariable fan speeds based on the temperature and zone load in the indoor units, (2) adding a detailedrefrigerant pipe heat loss calculation using refrigerant flow rate, operational conditions, pipe length, andpipe insulation materials, (3) improving accuracy of simulation especially in partial load conditions, and(4) improving the usability of the model by significantly reducing the number of user input performancecurves. The VRF-HP model is implemented in EnergyPlus and validated with measured data from fieldtests. Results show that the new VRF-HP model provides more accurate estimate of the VRF-HP systemperformance, which is key to determining code compliance credits as well as utilities incentive for VRFtechnologies.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-1004499</style></custom2><section><style face="normal" font="default" size="100%">399</style></section></record></records></xml>