Article
Construction & Building Technology
Leila Nikdel, Alan E. S. Schay, Daqing Hou, Susan E. Powers
Summary: The study presents occupancy profiles for apartment-style student housing based on high-resolution monitored data, showing a variety of occupancy patterns among students. Evaluating the sensitivity of predicted savings associated with occupancy-based heating and cooling controls to differences in occupancy schedules. The results demonstrate differences between data-driven occupancy patterns and the widely-used reference occupancy schedule.
ENERGY AND BUILDINGS
(2021)
Article
Construction & Building Technology
Shuqin Chen, Yinyan Lv, Zhichao Wang, Yuhang Ma, Yurui Huang, Yichao Wang, Yuxuan Cai, Zhiqin Rao
Summary: This study collects real-time occupancy data and develops a Monte Carlo-based model to simulate and compare the building heating and cooling load differences caused by fixed occupancy schedules and random occupancy time series. The results show that using random occupancy schedules can accurately predict and assess the building loads.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Construction & Building Technology
Debrudra Mitra, Yiyi Chu, Kristen Cetin
Summary: This study evaluated variations in typical types of occupancy schedules followed by the U.S. population using cluster analysis, identifying three main patterns that represent approximately 88% of people in the United States. The analysis focused on characteristics such as number of times leaving home, time of day leaving home, and timespan of absence, providing detailed insights on how occupants in the United States spend their time in residential spaces.
ENERGY AND BUILDINGS
(2021)
Article
Construction & Building Technology
Bongchan Jeong, Jungsoo Kim, Richard de Dear
Summary: This study analyzed occupancy and energy-related activities in Australian households based on a detailed time use survey, finding that occupancy and energy-related activities are time dependent and influenced by season, day of week, and household composition. The results can be used as input for simulation tools to improve accuracy in predicting building energy demand.
ENERGY AND BUILDINGS
(2021)
Review
Construction & Building Technology
Shayan Nejadshamsi, Ursula Eicker, Chun Wang, Jamal Bentahar
Summary: Buildings' occupant profiles at the urban scale are important for applications like Urban Building Energy Modeling (UBEM) and assessing energy consumption patterns. Estimating building occupancy profiles at the urban scale is challenging due to data availability and cost issues. This paper comprehensively reviews different aspects of building occupant profiles, approaches, and available data sources, and explores future research directions.
BUILDING AND ENVIRONMENT
(2023)
Review
Health Care Sciences & Services
Hassan A. Alsugair, Ibrahim F. Alshugair, Turki J. Alharbi, Abdulaziz M. Bin Rsheed, Ayla M. Tourkmani, Wedad Al-Madani
Summary: Semaglutide demonstrated significant superiority over liraglutide in long-term glycemic control and weight reduction. Further large-scale, well-designed randomized controlled trials are needed to confirm these findings due to potential limitations in the current studies.
Article
Business
Angie Higuchi, Rocio Maehara
Summary: This study aims to identify the motivational profile of quinoa consumers in Modern Metropolitan Lima through investigating the reasons for consuming quinoa. The outcomes of this research can help enhance strategies developed by the Peruvian Government, quinoa food companies, retailers, and quinoa producers to promote quinoa consumption.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Psychology, Multidisciplinary
Anke Boone, Tinne Vander Elst, Sofie Vandenbroeck, Lode Godderis
Summary: Burnout is a significant issue among young researchers, with cynicism being a common profile. Work-life interference and perceived publication pressure are key predictors of burnout risk, while meaningfulness, social support from supervisors, and learning opportunities play a protective role.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Information Systems
Thomas Graichen, Julia Richter, Rebecca Schmidt, Ulrich Heinkel
Summary: There is a growing interest in indoor positioning due to increasing applications employing position data. Current approaches face challenges with sensor data accuracy, but a solution involving occupancy grid maps (OGMs) modeling object probabilities in specific environments shows promise. Proposed algorithms for automated OGM generation using OpenStreetMap indoor data and improving positioning results have been found to increase indoor positioning accuracy.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2021)
Article
Construction & Building Technology
Jiasha Fu, Shan Hu, Xin He, Shunsuke Managi, Da Yan
Summary: This study conducted a national-wide survey in China to identify occupancy patterns in residential buildings and describe family profiles of typical occupancy pattern. The results showed that the average occupied duration for urban residential buildings is 20.3 hours per day during weekdays and 20.1 hours per day during weekends. Four occupancy patterns during workdays and five occupancy patterns during weekends were identified. Family demographic characteristics, housing characteristics, geographical location, education experience, work experience, and income level were found to be correlated with residents' occupancy pattern.
ENERGY AND BUILDINGS
(2022)
Article
Immunology
Jakob Hoeppner, Christoph Tabeling, Vincent Casteleyn, Claudia Kedor, Wolfram Windisch, Gerd Ruediger Burmester, Doerte Huscher, Elise Siegert
Summary: The aim of this study was to comprehensively analyze the serum autoantibody status in patients with systemic sclerosis (SSc) and correlate it with the clinical course of the disease. The study found that specific autoantibodies are associated with specific clinical manifestations, while the association of some rare antibodies is not fully clarified. The results of this study reveal important associations between the serologic status of SSc patients and disease manifestation, comorbidities, and complications.
FRONTIERS IN IMMUNOLOGY
(2023)
Article
Computer Science, Information Systems
Yonghao Zhao, Zhixiang Zhang, Tianyi Feng, Wai-Choong Wong, Hari Krishna Garg
Summary: GraphIPS is a calibration-free and map-free indoor positioning system that dynamically generates accurate radio maps by utilizing smartphone crowdsourced WiFi and IMU data. This system fuses crowdsourced data into a graph-based formulation and applies the MDS algorithm to compute the positions of the user's steps, achieving comparable accuracy to calibration-based methods.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Green & Sustainable Science & Technology
Zoltan Kovacs, Gyorgy Vida, Abel Elekes, Tamas Kovalcsik
Summary: This study combines social media and mobile positioning data to analyze international tourism flows in Szeged, Hungary, identifying specific events attracting foreign tourists and defining visitor peaks. By jointly applying social media and mobile positioning analytical tools, attractive tourism attractions can be identified, and movements of foreign visitors can be analyzed and evaluated.
Article
Endocrinology & Metabolism
Hui Gao, Kan Wang, Wensui Zhao, Jianlin Zhuang, Yu Jiang, Lei Zhang, Qingping Liu, Fariba Ahmadizar
Summary: This study examines the applicability of a data-driven clustering approach for type 2 diabetes (T2D) in the Chinese population, and investigates the cardiorenal risk profiles among different T2D sub-phenotypes. The study finds that different T2D sub-phenotypes have distinct cardiorenal risk profiles, and cardiovascular risk development occurs long before diabetes diagnosis.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Aleksey Ogulenko, Itzhak Benenson, Itzhak Omer, Barak Alon
Summary: The traditional method of mobile phone positioning relies on cell tower location as a proxy for device location, while our probabilistic approach is based on antenna parameters and connection numbers to address issues such as overlapping service areas and network load balancing. Through Bayesian inference, we are able to construct a more realistic distribution of device location, posing new challenges for mobile phone privacy and data analysis tools.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
(2021)
Review
Construction & Building Technology
Xuyuan Kang, Jingjing An, Da Yan
Summary: The building sector plays a significant role in energy consumption and carbon emissions. Increasing the use of renewable energy in buildings is crucial for achieving carbon neutrality. This study reviews the current status and future challenges of building electricity use profile models and highlights the importance of understanding building electricity use profiles for energy system design and electricity distribution network planning. The paper discusses key perspectives for future research, including model temporal scales, aggregation of electricity use profiles, occupant-driven environment-interactive model structures, and appropriate model evaluation. It aims to inform and inspire future research on building electricity use profiles and support the design of sustainable building systems and energy distribution networks.
ENERGY AND BUILDINGS
(2023)
Article
Green & Sustainable Science & Technology
Fan Bu, Da Yan, Gang Tan, Hongsan Sun, Jingjing An
Summary: This study proposed four algorithms to simplify the integral process and achieve passive cooling effect with higher accuracy and time saving. Approximately 50-98% of the computational time consumption has been saved for the four RCMs. The polynomial approximation algorithm performs best with an acceptable error in net long-wavelength radiation transfer of 0.16-4.33 W/m2. The optimized incorporation algorithm in this study could promote the simulation of RCMs in large city-scale application scenarios, such as urban heat island mitigation and heat wave impact.
Article
Thermodynamics
Yuxin Lu, Xinyu Yang, Xin Zhou, Jingjing An, Xiaomin Wang, Kun Zhang, Da Yan
Summary: Occupant control behavior is a crucial factor in determining the energy consumption of building air-conditioners. This study proposes a survival model to describe the turning on behavior of air-conditioners in office buildings. The model considers the cumulative effect of time on AC turning on behavior and predicts the probability of turning on based on environmental factors. The results show that the survival model outperforms the traditional Weibull model, resulting in improved prediction accuracy.
BUILDING SIMULATION
(2023)
Article
Construction & Building Technology
Xin Zhou, Yuxin Lu, Shan Hu, Ziyi Yang, Da Yan
Summary: This study analyzed the occupancy patterns of residential buildings based on time-use survey data. It compared the data from 2008 and 2017 to reveal temporal changes in occupancy patterns and explore the influencing factors of behavioral change. The study found that residents spent more time at home on workdays and less time at home on weekends. Additionally, residents shifted from the living room to the bedroom in their homes. Age, education level, and employment status were identified as factors affecting the length of time spent at home.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Construction & Building Technology
Yi Wu, Xin Zhou, Mingyang Qian, Yuan Jin, Hongsan Sun, Da Yan
Summary: A novel method is proposed to establish realistic air-conditioning (AC) behavior models and extract typical patterns based on large-scale on-site AC operation data. The study identifies two occupancy patterns and extracts five typical AC behavior patterns. The typical patterns improve the accuracy by 30.4% compared to fixed AC schedule models and can be integrated into building performance simulations and engineering projects.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Construction & Building Technology
Changcheng Chen, Jingjing An, Chuang Wang, Xiaorong Duan, Shiyu Lu, Hangyu Che, Meiwei Qi, Da Yan
Summary: Indoor temperature and relative humidity control in office buildings is crucial for thermal comfort, work efficiency, and occupant health. Conventional control methods often overlook the impact of indoor relative humidity by focusing solely on temperature. This study proposes a deep reinforcement learning algorithm for adjusting the air supply volume of fan coil units (FCUs) in office buildings, considering both temperature and relative humidity. The algorithm was trained, tested, and evaluated using a simulation environment model, and its results showed improved control satisfaction rates compared to traditional controllers.
Article
Construction & Building Technology
Zehongyu Kang, Hua Liu, Yuxin Lu, Xinyu Yang, Xin Zhou, Jingjing An, Da Yan, Xing Jin, Xing Shi
Summary: This study developed a novel approach for classifying water bodies based on their cooling effect and established a prediction model using machine learning methods. The proposed model accurately determined the cooling effect classification of water bodies and identified effective measures for improving the cooling effect. This research provides a basis for using water bodies' cooling effect more effectively and guiding urban planning and waterfront construction.
BUILDING AND ENVIRONMENT
(2023)
Article
Ecology
Yang Zhang, Shan Hu, Da Yan, Yi Jiang
Summary: This study reviewed the shortcomings in existing production-based, consumption-based, and certain shared methods for carbon emission sharing responsibility and proposed a novel benchmark approach that fits effectiveness, normalization, monotony, and feasibility principles. Two case applications were implemented to verify the feasibility of this method, showing that the benchmark approach is more effective in leading carbon emission reduction measures from both producer and consumer sides. The establishment of a benchmark value system and corresponding carbon emission reduction policy instruments are recommended for future research.
ECOLOGICAL ECONOMICS
(2023)
Article
Construction & Building Technology
Fan Bu, Xuyuan Kang, Da Yan, Ruhong Wu, Hongsan Sun, Jingjing An, Xiao Wang
Summary: Building thermal process simulation is an effective technique for analyzing building energy consumption. This study proposes a new computing architecture to optimize the state-space method in building energy simulation tools. Parallelizing the simulation process significantly accelerates the computing power.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Chenyang Peng, Zhihua Chen, Jingjing Yang, Zhaoru Liu, Da Yan, Yixing Chen
Summary: This paper evaluates the electricity consumption reduction potential for buildings in Changsha under different demand response strategies. Sixtysix prototype energy models for twenty-two building types were selected to represent 59,332 buildings in Changsha, and three demand response strategies were implemented. The results show that the electricity consumption reductions range from 7.94% to 22.00% under different strategies and their combinations.
ENERGY AND BUILDINGS
(2023)
Review
Thermodynamics
Xin Zhou, Ruoxi Liu, Shuai Tian, Xiaohan Shen, Xinyu Yang, Jingjing An, Da Yan
Summary: This paper systematically analyzes the current application status of validation methods for building energy modeling programs (BEMPs), summarizes the characteristics and applicability of different methods in different stages, and provides guidance for choosing suitable validation methods and evaluation indicators.
BUILDING SIMULATION
(2023)
Article
Thermodynamics
Jingjing An, Yi Wu, Chenxi Gui, Da Yan
Summary: This study developed 151 prototype building models and a large database of 9225 models to simulate building energy in different cities. These models can be used for building energy conservation research, including analysis of energy-saving technologies, advanced controls, and new policies, as well as providing a reference for the development of building energy codes and standards.
BUILDING SIMULATION
(2023)
Article
Construction & Building Technology
Yi Wu, Jingjing An, Mingyang Qian, Da Yan
Summary: This study proposes a systematic technical framework for determining the appropriate level of detail for modeling occupant air-conditioning use behavior in building energy simulation. The framework includes defining applications and key performance indicators, identifying comparable levels of detail, and selecting the appropriate level based on analysis. Different levels of detail are recommended for different applications, providing guidance for future projects.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Construction & Building Technology
Xiao Wang, Xuyuan Kang, Jingjing An, Hanran Chen, Da Yan
Summary: This study proposes a reinforcement learning approach for optimal control of ice-based thermal energy storage systems in commercial buildings. By using RL framework, deep Q-network, and environment simulation, this method can effectively reduce costs and improve the energy efficiency of ice-based TES systems.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Samiran Khorat, Debashish Das, Rupali Khatun, Sk Mohammad Aziz, Prashant Anand, Ansar Khan, Mattheos Santamouris, Dev Niyogi
Summary: Cool roofs can effectively mitigate heatwave-induced excess heat and enhance thermal comfort in urban areas. Implementing cool roofs can significantly improve urban meteorology and thermal comfort, reducing energy flux and heat stress.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Qi Li, Jiayu Chen, Xiaowei Luo
Summary: This study focuses on the vertical wind conditions as a main external factor that limits the energy assessment of high-rise buildings in urban areas. Traditional tools for energy assessment of buildings use a universal vertical wind profile estimation, without taking into account the unique wind speed in each direction induced by the various shapes and configurations of buildings in cities. To address this limitation, the study developed an omnidirectional urban vertical wind speed estimation method using direction-dependent building morphologies and machine learning algorithms.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Xiaojun Luo, Lamine Mahdjoubi
Summary: This paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy allocation and transmission among multiple domestic buildings. Machine learning is used to predict energy generation and consumption patterns, and the proposed framework establishes optimal and automated energy allocation through peer-to-peer energy transactions. The approach contributes to the reduction of greenhouse gas emissions and enhances environmental sustainability.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ying Yu, Yuanwei Xiao, Jinshuai Chou, Xingyu Wang, Liu Yang
Summary: This study proposes a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Case studies show that the proposed design significantly improves the performance of building clusters, reduces energy storage capacity, and shortens the payback period.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Felix Langner, Weimin Wang, Moritz Frahm, Veit Hagenmeyer
Summary: This paper compares two main approaches to consider uncertainties in model predictive control (MPC) for buildings: robust and stochastic MPC. The results show that compared to a deterministic MPC, the robust MPC increases the electricity cost while providing complete temperature constraint satisfaction, while the stochastic MPC slightly increases the electricity cost but fulfills the thermal comfort requirements.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Somil Yadav, Caroline Hachem-Vermette
Summary: This study proposes a mathematical model to evaluate the performance of a Double Skin Facade (DSF) system and its impact on indoor conditions. The model considers various design parameters and analyzes their effects on the system's electrical output and room temperature.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ruijun Chen, Holly Samuelson, Yukai Zou, Xianghan Zheng, Yifan Cao
Summary: This research introduces an innovative resilient design framework that optimizes building performance by considering a holistic life cycle perspective and accounting for climate projection uncertainties. The study finds that future climate scenarios significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density being major factors. By using ensemble learning and optimization algorithms, predictions for carbon emissions, cost, and indoor discomfort hours can be made, and the best resilient design scheme can be selected. Applying this framework leads to significant improvements in building life cycle performance.
ENERGY AND BUILDINGS
(2024)
