02259nas a2200265 4500008004100000022001300041245013000054210006900184260001200253300001400265490000800279520141300287653002901700653002101729653002701750653001901777653003601796100001401832700001901846700001701865700001601882700001501898700001301913856006701926 2019 eng d a0306261900aForecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm0 aForecasting districtscale energy dynamics through integrating bu c04/2019 a217 - 2300 v2483 a

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.

10aBuilding Energy Modeling10aBuilding network10aData-driven prediction10aDistrict-scale10aLong short-term memory networks1 aWang, Wei1 aHong, Tianzhen1 aXu, Xiaodong1 aChen, Jiayu1 aLiu, Ziang1 aXu, Ning uhttps://linkinghub.elsevier.com/retrieve/pii/S030626191930749402515nas a2200241 4500008004100000022001300041245010300054210006900157260001200226300001200238490000800250520174400258653003102002653002102033653003902054653002602093653002102119100001702140700001402157700001902171700001602190856006702206 2019 eng d a0378778800aIncorporating machine learning with building network analysis to predict multi-building energy use0 aIncorporating machine learning with building network analysis to c06/2019 a80 - 970 v1863 a

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.

10aArtificial neural networks10aBuilding network10acold winter and hot summer climate10aEnergy use prediction10aMachine learning1 aXu, Xiaodong1 aWang, Wei1 aHong, Tianzhen1 aChen, Jiayu uhttps://linkinghub.elsevier.com/retrieve/pii/S037877881831976502424nas a2200241 4500008004100000022001300041245013600054210006900190260001200259300001200271490000800283520161400291653002301905653003401928653002301962653002701985100001402012700001902026700001202045700001902057700001602076856009002092 2019 eng d a0306261900aLinking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification0 aLinking energycyberphysical systems with occupancy prediction an c02/2019 a55 - 690 v2363 a

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.

10aBuilding occupancy10aEnergy-Cyber-Physical Systems10aensemble algorithm10aWi-Fi probe technology1 aWang, Wei1 aHong, Tianzhen1 aLi, Nan1 aWang, Ryan, Qi1 aChen, Jiayu uhttps://simulationresearch.lbl.gov/publications/linking-energy-cyber-physical-systems02290nas a2200193 4500008004100000022001400041245012000055210006900175260001200244300001400256490000700270520148300277100001701760700001401777700001401791700001901805700001301824856025901837 2019 eng d a1996-359900aPerformance-driven optimization of urban open space configuration in the cold-winter and hot-summer region of China0 aPerformancedriven optimization of urban open space configuration c03/2019 a411 - 4240 v123 a

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.

1 aXu, Xiaodong1 aWu, Yifan1 aWang, Wei1 aHong, Tianzhen1 aXu, Ning uhttp://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.html02433nas a2200193 4500008004100000245009600041210006900137490000800206520175100214653002201965653001501987653004302002653002702045653002802072100001402100700001602114700001902130856009002149 2018 eng d00aModeling occupancy distribution in large spaces with multi-feature classification algorithm0 aModeling occupancy distribution in large spaces with multifeatur0 v1373 a

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' spatial distribution, 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%.

10aenergy efficiency10aHVAC loads10amulti-feature classification algorithm10aoccupancy distribution10aoccupancy-based control1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-occupancy-distribution-large02356nas a2200229 4500008004100000022001300041245012200054210006900176260001200245300001400257490000700271520146000278653001601738653002601754653002101780653002501801653001801826100001401844700001601858700001901874856023301893 2018 eng d a0926580500aOccupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings0 aOccupancy prediction through machine learning and data fusion of c10/2018 a233 - 2430 v943 a

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' 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' 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.

10adata fusion10aenvironmental sensing10aMachine learning10aoccupancy prediction10aWi-Fi sensing1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen uhttps://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/plain02098nas a2200181 4500008004100000022001300041245012600054210006900180260001200249300001400261490000800275520133900283100001401622700001601636700001901652700001201671856023301683 2018 eng d a0360132300aOccupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology0 aOccupancy prediction through Markov based feedback recurrent neu c06/2018 a160 - 1700 v1383 a

Accurate occupancy prediction can improve facility control and energy efficiency of buildings. In recent years, buildings' 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's occupancy profile.

1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen1 aZhu, Na uhttps://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/plain01861nas a2200241 4500008004100000245010500041210006900146260001200215300000900227490000700236520110600243653001701349653002101366653002701387653002201414653001101436100001701447700001601464700001401480700001901494700001701513856008901530 2018 eng d00aPerformance-Based Evaluation of Courtyard Design in China’s Cold-Winter Hot-Summer Climate Regions0 aPerformanceBased Evaluation of Courtyard Design in China s ColdW c10/2018 a39500 v103 a

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.

10aaspect ratio10acourtyard design10aecological buffer area10aecological effect10alayout1 aXu, Xiaodong1 aLuo, Fenlan1 aWang, Wei1 aHong, Tianzhen1 aFu, Xiuzhang uhttp://www.mdpi.com/2071-1050/10/11/3950http://www.mdpi.com/2071-1050/10/11/3950/pdf