@article {31670, title = {Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm}, journal = {Applied Energy}, volume = {248}, year = {2019}, month = {04/2019}, pages = {217 - 230}, abstract = {

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.

}, keywords = {Building Energy Modeling, Building network, Data-driven prediction, District-scale, Long short-term memory networks}, issn = {03062619}, doi = {10.1016/j.apenergy.2019.04.085}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261919307494}, author = {Wei Wang and Tianzhen Hong and Xiaodong Xu and Jiayu Chen and Ziang Liu and Ning Xu} } @article {31665, title = {Incorporating machine learning with building network analysis to predict multi-building energy use}, journal = {Energy and Buildings}, volume = {186}, year = {2019}, month = {06/2019}, pages = {80 - 97}, abstract = {

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.

}, keywords = {Artificial neural networks, Building network, cold winter and hot summer climate, Energy use prediction, Machine learning}, issn = {03787788}, doi = {10.1016/j.enbuild.2019.01.002}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818319765}, author = {Xiaodong Xu and Wei Wang and Tianzhen Hong and Jiayu Chen} } @article {31662, title = {Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification}, journal = {Applied Energy}, volume = {236}, year = {2019}, month = {02/2019}, pages = {55 - 69}, abstract = {

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.

}, keywords = {Building occupancy, Energy-Cyber-Physical Systems, ensemble algorithm, Wi-Fi probe technology}, issn = {03062619}, doi = {10.1016/j.apenergy.2018.11.079}, author = {Wei Wang and Tianzhen Hong and Nan Li and Ryan Qi Wang and Jiayu Chen} } @article {30488, title = {Modeling occupancy distribution in large spaces with multi-feature classification algorithm}, journal = {Building and Environment}, volume = {137}, year = {2018}, abstract = {

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{\textquoteright} 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\%.

}, keywords = {energy efficiency, HVAC loads, multi-feature classification algorithm, occupancy distribution, occupancy-based control}, doi = {10.1016/j.buildenv.2018.04.002}, author = {Wei Wang and Jiayu Chen and Tianzhen Hong} } @article {31309, title = {Occupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings}, journal = {Automation in Construction}, volume = {94}, year = {2018}, month = {10/2018}, pages = {233 - 243}, abstract = {

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{\textquoteright} 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{\textquoteright} 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.

}, keywords = {data fusion, environmental sensing, Machine learning, occupancy prediction, Wi-Fi sensing}, issn = {09265805}, doi = {10.1016/j.autcon.2018.07.007}, url = {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}, author = {Wei Wang and Jiayu Chen and Tianzhen Hong} } @article {31304, title = {Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology}, journal = {Building and Environment}, volume = {138}, year = {2018}, month = {06/2018}, pages = {160 - 170}, abstract = {

Accurate occupancy prediction can improve facility control and energy efficiency of buildings. In recent years, buildings{\textquoteright} 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{\textquoteright}s occupancy profile.

}, issn = {03601323}, doi = {10.1016/j.buildenv.2018.04.034}, url = {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}, author = {Wei Wang and Jiayu Chen and Tianzhen Hong and Na Zhu} }