<?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%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xiaodong Xu</style></author><author><style face="normal" font="default" size="100%">Jiayu Chen</style></author><author><style face="normal" font="default" size="100%">Ziang Liu</style></author><author><style face="normal" font="default" size="100%">Ning Xu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm</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%">Building Energy Modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Building network</style></keyword><keyword><style  face="normal" font="default" size="100%">Data-driven prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">District-scale</style></keyword><keyword><style  face="normal" font="default" size="100%">Long short-term memory networks</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%">04/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0306261919307494</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">248</style></volume><pages><style face="normal" font="default" size="100%">217 - 230</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;With the development of data-driven techniques, district-scale building energy prediction has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m2, compared to 12.05% and 0.63 kWh/m2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m2 for the support vector regression model.&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%">Xiaodong Xu</style></author><author><style face="normal" font="default" size="100%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Jiayu Chen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Incorporating machine learning with building network analysis to predict multi-building energy use</style></title><secondary-title><style face="normal" font="default" size="100%">Energy and Buildings</style></secondary-title><short-title><style face="normal" font="default" size="100%">Energy and Buildings</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Building network</style></keyword><keyword><style  face="normal" font="default" size="100%">cold winter and hot summer climate</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy use prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine learning</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%">06/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0378778818319765</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">186</style></volume><pages><style face="normal" font="default" size="100%">80 - 97</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Predicting multi-building energy use at campus or city district scale has recently gained more attention; and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67% for the office group, 90.79% for the educational group, 92.34% for the laboratory group, and 83.32% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.&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%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Nan Li</style></author><author><style face="normal" font="default" size="100%">Ryan Qi Wang</style></author><author><style face="normal" font="default" size="100%">Jiayu Chen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification</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%">Building occupancy</style></keyword><keyword><style  face="normal" font="default" size="100%">Energy-Cyber-Physical Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">ensemble algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Wi-Fi probe technology</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><volume><style face="normal" font="default" size="100%">236</style></volume><pages><style face="normal" font="default" size="100%">55 - 69</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;With rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most prominent platforms to improve building design and management. Researchers investigated the possibility of integrating energy management system with cyber-physical systems as energy-cyber-physical systems to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is one major source of uncertainties for energy management, ignoring it often results in energy wastes caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposed an occupancy linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilized ensemble classification algorithms to extract three types of occupancy information. It creates a data interface to link energy management system and cyber-physical systems and allows automated occupancy detection and interpretation through assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy type, the proposed model can potentially save about 26.4% of energy consumption from the cooling and ventilation demands.&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%">Xiaodong Xu</style></author><author><style face="normal" font="default" size="100%">Yifan Wu</style></author><author><style face="normal" font="default" size="100%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Ning Xu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Performance-driven optimization of urban open space configuration in the cold-winter and hot-summer region of China</style></title><secondary-title><style face="normal" font="default" size="100%">Building Simulation</style></secondary-title><short-title><style face="normal" font="default" size="100%">Build. Simul.</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/10.1007/s12273-019-0510-zhttp://link.springer.com/content/pdf/10.1007/s12273-019-0510-z.pdfhttp://link.springer.com/content/pdf/10.1007/s12273-019-0510-z.pdfhttp://link.springer.com/article/10.1007/s12273-019-0510-z/fulltext.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">411 - 424</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Urbanization has led to changes in urban morphology and climate, while urban open space has become an important ecological factor for evaluating the performance of urban development. This study presents an optimization approach using computational performance simulation. With a genetic algorithm using the Grasshopper tool, this study analyzed the layout and configuration of urban open space and its impact on the urban micro-climate under summer and winter conditions. The outdoor mean Universal Thermal Climate Index (UTCI) was applied as the performance indicator for evaluating the quality of the urban micro-climate. Two cases—one testbed and one real urban block in Nanjing, China—were used to validate the computer-aided simulation process. The optimization results in the testbed showed UTCI values varied from 36.5 to 37.3 °C in summer and from −4.9 to −1.9 °C in winter. In the case of the real urban block, optimization results show, for summer, although the average UTCI value increased by 0.6 °C, the average air velocity increased by 0.2 m/s; while in winter, the average UTCI value increased by 1.7 °C and the average air velocity decreased by 0.2 m/s. These results demonstrate that the proposed computer-aided optimization process can improve the thermal comfort conditions of open space in urban blocks. Finally, this study discusses strategies and guidelines for the layout design of urban open space to improve urban environment comfort.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></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%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Jiayu Chen</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%">Modeling occupancy distribution in large spaces with multi-feature classification algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">energy efficiency</style></keyword><keyword><style  face="normal" font="default" size="100%">HVAC loads</style></keyword><keyword><style  face="normal" font="default" size="100%">multi-feature classification algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">occupancy distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">occupancy-based control</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">137</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Occupancy information enables robust and flexible control of heating, ventilation, and air-conditioning (HVAC) systems in buildings. In large spaces, multiple HVAC terminals are typically installed to provide cooperative services for different thermal zones, and the occupancy information determines the cooperation among terminals. However, a person count at room-level does not adequately optimize HVAC system operation due to the movement of occupants within the room that creates uneven load distribution. Without accurate knowledge of the occupants&#039; &lt;a title=&quot;Learn more about spatial distribution&quot; href=&quot;https://www.sciencedirect.com/topics/earth-and-planetary-sciences/spatial-distribution&quot; target=&quot;_blank&quot;&gt;spatial distribution&lt;/a&gt;, the uneven distribution of occupants often results in under-cooling/heating or over-cooling/heating in some thermal zones. Therefore, the lack of high-resolution occupancy distribution is often perceived as a bottleneck for future improvements to HVAC operation efficiency. To fill this gap, this study proposes a multi-feature k-Nearest-Neighbors (k-NN) classification algorithm to extract occupancy distribution through reliable, low-cost Bluetooth Low Energy (BLE) networks. An on-site experiment was conducted in a typical office of an institutional building to demonstrate the proposed methods, and the experiment outcomes of three case studies were examined to validate detection accuracy. One method based on City Block Distance (CBD) was used to measure the distance between detected occupancy distribution and ground truth and assess the results of occupancy distribution. The results show the accuracy when CBD = 1 is over 71.4% and the accuracy when CBD = 2 can reach up to 92.9%.&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%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Jiayu Chen</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%">Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Automation in Construction</style></secondary-title><short-title><style face="normal" font="default" size="100%">Automation in Construction</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%">environmental sensing</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">occupancy prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">Wi-Fi sensing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0926580518302656https://api.elsevier.com/content/article/PII:S0926580518302656?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0926580518302656?httpAccept=text/plain</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">94</style></volume><pages><style face="normal" font="default" size="100%">233 - 243</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Occupancy information is crucial to building facility design, operation, and energy efficiency. Many studies propose the use of environmental sensors (such as carbon dioxide, air temperature, and relative humidity sensors) and radio-frequency sensors (Wi-Fi networks) to monitor, assess, and predict occupancy information for buildings. As many methods have been developed and a variety of sensory data sources are available, establishing a proper selection of model and data source is critical to the successful implementation of occupancy prediction systems. This study compared three popular machine learning algorithms, including k-nearest neighbors (kNN), support vector machine (SVM), and artificial neural network (ANN), combined with three data sources, including environmental data, Wi-Fi data, and fused data, to optimize the occupancy models&#039; performance in various scenarios. Three error measurement metrics, the mean average error (MAE), mean average percentage error (MAPE), and root mean squared error (RMSE), have been employed to compare the models&#039; accuracies. Examined with an on-site experiment, the results suggest that the ANN-based model with fused data has the best performance, while the SVM model is more suitable with Wi-Fi data. The results also indicate that, comparing with independent data sources, the fused data set does not necessarily improve model accuracy but shows a better robustness for occupancy prediction.&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%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Jiayu Chen</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Na Zhu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology</style></title><secondary-title><style face="normal" font="default" size="100%">Building and Environment</style></secondary-title><short-title><style face="normal" font="default" size="100%">Building and Environment</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S0360132318302464https://api.elsevier.com/content/article/PII:S0360132318302464?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0360132318302464?httpAccept=text/plain</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">138</style></volume><pages><style face="normal" font="default" size="100%">160 - 170</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Accurate occupancy prediction can improve facility control and energy efficiency of buildings. In recent years, buildings&#039; exiting WiFi infrastructures have been widely studied in the research of occupancy and energy conservation. However, using WiFi to assess occupancy is challenging due to that occupancy information is often characterized stochastically and varies with time and easily disturbed by building components. To overcome such limitations, this study utilizes WiFi probe technology to actively scan WiFi connection requests and responses between access points and network devices of building occupants. With captured signals, this study proposed a Markov based feedback recurrent neural network (M-FRNN) algorithm to model and predict the occupancy profiles. One on-site experiment was conducted to collect ground truth data using camera-based video analysis and the results were used to validate the M-FRNN occupancy prediction model over a 9-day measurement period. From the results, the M-FRNN based occupancy model using WiFi probes shows best accuracies can reach 80.9%, 89.6%, and 93.9% with a tolerance of 2, 3, and 4 occupants respectively. This study demonstrated that WiFi data coupled with stochastic machine learning system can provide a viable alternative to determine a building&#039;s occupancy profile.&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%">Xiaodong Xu</style></author><author><style face="normal" font="default" size="100%">Fenlan Luo</style></author><author><style face="normal" font="default" size="100%">Wei Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Xiuzhang Fu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Performance-Based Evaluation of Courtyard Design in China’s Cold-Winter Hot-Summer Climate Regions</style></title><secondary-title><style face="normal" font="default" size="100%">Sustainability</style></secondary-title><short-title><style face="normal" font="default" size="100%">Sustainability</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">aspect ratio</style></keyword><keyword><style  face="normal" font="default" size="100%">courtyard design</style></keyword><keyword><style  face="normal" font="default" size="100%">ecological buffer area</style></keyword><keyword><style  face="normal" font="default" size="100%">ecological effect</style></keyword><keyword><style  face="normal" font="default" size="100%">layout</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.mdpi.com/2071-1050/10/11/3950http://www.mdpi.com/2071-1050/10/11/3950/pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">3950</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Evaluates the performance of the traditional courtyard design of the Jiangnan Museum, located in Jiangsu Province. In the evaluation, the spatial layout of courtyards is adjusted, the aspect ratio is changed, and an ecological buffer space is created. To model and evaluate the performance of the courtyard design, this study applied the Computational fluid dynamics (CFD) software, Parabolic Hyperbolic Or Elliptic Numerical Integration Code Series (PHOENICS), for wind environment simulation, and the EnergyPlus-based software, DesignBuilder, for energy simulation. Results show that a good combination of courtyard layout and aspect ratio can improve the use of natural ventilation by increasing free cooling during hot summers and reducing cold wind in winters. The results also show that ecological buffer areas of a courtyard can reduce cooling loads in summer by approximately 19.6% and heating loads in winter by approximately 22.3%. The study provides insights into the optimal design of a courtyard to maximize its benefit in regulating the microclimate during both winter and summer.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue></record></records></xml>