<?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%">Zhe Wang</style></author><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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data fusion in predicting internal heat gains for office buildings through a deep learning approach</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Energy</style></secondary-title><short-title><style face="normal" font="default" size="100%">Applied Energy</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data fusion</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Internal heat gains</style></keyword><keyword><style  face="normal" font="default" size="100%">Miscellaneous electric loads</style></keyword><keyword><style  face="normal" font="default" size="100%">Occupant count</style></keyword><keyword><style  face="normal" font="default" size="100%">Predictive control</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0306261919303630</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">240</style></volume><pages><style face="normal" font="default" size="100%">386 - 398</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Heating, Ventilation, and Air Conditioning (HVAC) is a major energy consumer in buildings. The predictive control has demonstrated a potential to reduce HVAC energy use. To facilitate predictive HVAC control, internal heat gains prediction is required. In this study, we applied Long Short-Term Memory Networks, a special form of deep neural network, to predict miscellaneous electric loads, lighting loads, occupant counts and internal heat gains in two United States office buildings. Compared with the predetermined schedules used in American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard 90.1, the Long Short-Term Memory Networks method could reduce the prediction errors of internal heat gains from 12% to 8% in Building A, and from 26% to 16% in Building B. It was also found that for internal heat gains prediction, miscellaneous electric loads is a more important feature than occupant counts for two reasons. First, miscellaneous electric loads is the best proxy variable for internal heat gains, as it is the major component of and has the highest correlation coefficient with the internal heat gains. Second, miscellaneous electric loads contain valuable information to predict occupant count, while occupant count could not help improve miscellaneous electric loads prediction. These findings could help researchers and practitioners select the most relevant features to more accurately predict internal heat gains for the implementation of predictive HVAC control in buildings.&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%">Sang Hoon Lee</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Geof Sawaya</style></author><author><style face="normal" font="default" size="100%">Yixing Chen</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DEEP: A Database of Energy Efficiency Performance to Accelerate Energy Retrofitting of Commercial Buildings</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The paper presents a method and process to establish a database of energy efficiency performance (DEEP) to enable quick and accurate assessment of energy retrofit of commercial buildings. DEEP was compiled from results of about 35 million EnergyPlus simulations. DEEP provides energy savings for screening and evaluation of retrofit measures targeting the small and medium-sized office and retail buildings in California. The prototype building models are developed for a comprehensive assessment of building energy performance based on DOE commercial reference buildings and the California DEER prototype buildings. The prototype buildings represent seven building types across six vintages of constructions and 16 California climate zones. DEEP uses these prototypes to evaluate energy performance of about 100 energy conservation measures covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and domestic hot water. DEEP consists the energy simulation results for individual retrofit measures as well as packages of measures to consider interactive effects between multiple measures. The large scale EnergyPlus simulations are being conducted on the super computers at the National Energy Research Scientific Computing Center of Lawrence Berkeley National Laboratory. The pre-simulation database is a part of an on-going project to develop a web-based retrofit toolkit for small and medium-sized commercial buildings in California, which provides real-time energy retrofit feedback by querying DEEP with recommended measures, estimated energy savings and financial payback period based on users’ decision criteria of maximizing energy savings, energy cost savings, carbon reduction, or payback of investment. The pre-simulated database and associated comprehensive measure analysis enhances the ability to performance assessments of retrofits to reduce energy use for small and medium buildings and business owners who typically do not have resources to conduct costly building energy audit. DEEP will be migrated into the DEnCity - DOE’s Energy City, which integrates large-scale energy data for multi-purpose, open, and dynamic database leveraging diverse source of existing simulation data.&lt;/p&gt;</style></abstract><custom2><style face="normal" font="default" size="100%">LBNL-180309</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sila Kiliccote</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author><author><style face="normal" font="default" size="100%">David S. Watson</style></author><author><style face="normal" font="default" size="100%">Glenn D. Hughes</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic Controls for Energy Efficiency and Demand Response: Framework Concepts and a New Construction Case Study in New York</style></title><secondary-title><style face="normal" font="default" size="100%">2006 ACEEE Summer Study on Energy Efficiency in Buildings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2006</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Pacific Grove, CA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Satkartar Kinney</style></author><author><style face="normal" font="default" size="100%">Mary Ann Piette</style></author><author><style face="normal" font="default" size="100%">Lixing Gu</style></author><author><style face="normal" font="default" size="100%">Philip Haves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Demand Relief and Weather Sensitivity in Large California Commercial Buildings</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference for Enhancing Building Operations</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2001</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Austin, TX</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A great deal of research has examined the weather sensitivity of energy consumption in commercial buildings; however, the recent power crisis in California has given greater importance to peak demand. Several new loadshedding programs have been implemented or are under consideration.  Historically, the target customers have been large industrial users who can reduce the equivalent load of several large office buildings. While the individual load reduction from an individual office building may be less significant, there is ample opportunity for load reduction in this area.  The load reduction programs and incentives for industrial customers may not be suitable for commercial building owners. In particular, industrial customers are likely to have little variation in load from day to day. Thus a robust baseline accounting for weather variability is required to provide building owners with realistic targets that will encourage them to participate in load shedding programs.&lt;/p&gt;</style></abstract></record></records></xml>