The Experts below are selected from a list of 71676 Experts worldwide ranked by ideXlab platform
Shuangping Duan - One of the best experts on this subject based on the ideXlab platform.
-
the impact of Building Operations on urban heat cool islands under urban densification a comparison between naturally ventilated and air conditioned Buildings
Applied Energy, 2019Co-Authors: Shuangping Duan, Zhiwen Luo, Xinyan YangAbstract:Abstract Many cities are suffering the effects of urban heat islands (UHI) or urban cool islands (UCI) due to rapid urban expansion and numerous infrastructure developments. This paper presents a lumped urban-Building thermal coupling model which captures the fundamental physical mechanism for thermal interactions between Buildings and their urban environment. The benefits of the model are its simplicity and high computational efficiency for practical use in investigating the diurnal urban air temperature change and its asymmetry in a city with both naturally-ventilated (NV) and air-conditioned (AC) Buildings. Our model predicts a lower urban heat island and higher urban cool island intensity in a city with naturally-ventilated Buildings than for a city with air-conditioned Buildings. During the urban densification (from a low-rise, low-density city to a high-rise, high-density one), the increases in the time constant and internal heat gain give rise to asymmetric warming phenomena, which become more obvious in a city with air-conditioned Buildings rather than naturally-ventilated ones. Unlike previous studies, we found that a low-rise, low-density city experiences a stronger urban cool island effect than a high-rise, high-density city due to less heat being emitted into the urban atmosphere. The urban cool/heat island effect will firstly increase/decrease, and then rapidly decrease/increase and ultimately disappear/dominate with increasing time constant in the process of urbanization/urban densification.
John E Taylor - One of the best experts on this subject based on the ideXlab platform.
-
bizwatts a modular socio technical energy management system for empowering commercial Building occupants to conserve energy
Applied Energy, 2014Co-Authors: Rimas Gulbinas, Rishee K Jain, John E TaylorAbstract:Abstract Commercial Buildings represent a significant portion of energy consumption and environmental emissions worldwide. To help mitigate the environmental impact of Building Operations, Building energy management systems and behavior-based campaigns designed to reduce energy consumption are becoming increasingly popular. In this paper, we describe the development of a modular socio-technical energy management system, BizWatts, which combines the two approaches by providing real-time, appliance-level power management and socially contextualized energy consumption feedback. We describe in detail the physical and virtual architecture of the system, which simultaneously engages Building occupants and facility managers, as well as the main principles behind the interface design and component functionalities. A discussion about how the data collection capabilities of the system enable insightful commercial Building energy efficiency studies and quantitative network analysis is also included. We conclude by commenting on the validation of the system, identifying current system limitations and introducing new research avenues that the development and deployment of BizWatts enables.
-
effects of real time eco feedback and organizational network dynamics on energy efficient behavior in commercial Buildings
Energy and Buildings, 2014Co-Authors: Rimas Gulbinas, John E TaylorAbstract:Abstract Commercial Buildings account for a significant portion of energy consumption and associated carbon emissions around the world. Consequently, many countries are instituting Building energy efficiency policies to mitigate the negative environmental impacts of Building Operations. As Building owners and operators act to address the challenge of increasing energy efficiency, occupant behavior modification programs are growing increasingly popular. Recent advances in energy monitoring and control technologies have enabled the development of eco-feedback systems that collect, process, and relay high resolution, real-time energy consumption information to help Building occupants control their energy-use. These systems have extended research into the effects of high resolution eco-feedback on Building occupant behavior and energy efficiency from residential to commercial Building settings. However, little is understood about how organizational network dynamics impact user-engagement levels with such systems and how these network connections may impact the energy conservation behavior of individuals inside commercial Buildings. In this paper, results are presented from a novel 9-week eco-feedback system study which demonstrates that organizational network dynamics can significantly impact energy conservation among commercial Building occupants. Furthermore, it is shown that exposure to eco-feedback impacts Building occupant energy conservation differently in commercial office Buildings than it does in residential Buildings.
Xinyan Yang - One of the best experts on this subject based on the ideXlab platform.
-
the impact of Building Operations on urban heat cool islands under urban densification a comparison between naturally ventilated and air conditioned Buildings
Applied Energy, 2019Co-Authors: Shuangping Duan, Zhiwen Luo, Xinyan YangAbstract:Abstract Many cities are suffering the effects of urban heat islands (UHI) or urban cool islands (UCI) due to rapid urban expansion and numerous infrastructure developments. This paper presents a lumped urban-Building thermal coupling model which captures the fundamental physical mechanism for thermal interactions between Buildings and their urban environment. The benefits of the model are its simplicity and high computational efficiency for practical use in investigating the diurnal urban air temperature change and its asymmetry in a city with both naturally-ventilated (NV) and air-conditioned (AC) Buildings. Our model predicts a lower urban heat island and higher urban cool island intensity in a city with naturally-ventilated Buildings than for a city with air-conditioned Buildings. During the urban densification (from a low-rise, low-density city to a high-rise, high-density one), the increases in the time constant and internal heat gain give rise to asymmetric warming phenomena, which become more obvious in a city with air-conditioned Buildings rather than naturally-ventilated ones. Unlike previous studies, we found that a low-rise, low-density city experiences a stronger urban cool island effect than a high-rise, high-density city due to less heat being emitted into the urban atmosphere. The urban cool/heat island effect will firstly increase/decrease, and then rapidly decrease/increase and ultimately disappear/dominate with increasing time constant in the process of urbanization/urban densification.
Cheng Fan - One of the best experts on this subject based on the ideXlab platform.
-
discovering gradual patterns in Building Operations for improving Building energy efficiency
Applied Energy, 2018Co-Authors: Cheng Fan, Fu Xiao, Yongjun Sun, Kui Shan, Jiayuan WangAbstract:Abstract The development of information technologies has enabled real-time monitoring and controls over Building Operations. Massive amounts of Building operational data are being collected and available for knowledge discovery. Advanced data analytics are urgently needed to fully realize the potentials of big Building operational data in enhancing Building energy efficiency. The rapid development of data mining has provided powerful tools for extracting insights in various knowledge representations. Gradual pattern mining is a promising technique for discovering useful patterns from Building operational data. The knowledge discovered is represented as gradual relationships, i.e., “the more/less A, the more/less B”. It can bring special interests to Building energy management by highlighting co-variations among numerical Building variables. This study investigated the usefulness of gradual pattern mining for Building energy management. A generic methodology was proposed to ensure the quality and applicability of the knowledge discovered. The methodology was validated through a case study. The results showed that the methodology could successfully extract valuable insights on Building operation characteristics and provide opportunities for Building energy efficiency enhancement.
-
analytical investigation of autoencoder based methods for unsupervised anomaly detection in Building energy data
Applied Energy, 2018Co-Authors: Cheng Fan, Fu Xiao, Yang Zhao, Jiayuan WangAbstract:Abstract Practical Building Operations usually deviate from the designed Building operational performance due to the wide existence of operating faults and improper control strategies. Great energy saving potential can be realized if inefficient or faulty Operations are detected and amended in time. The vast amounts of Building operational data collected by the Building Automation System have made it feasible to develop data-driven approaches to anomaly detection. Compared with supervised analytics, unsupervised anomaly detection is more practical in analyzing real-world Building operational data, as anomaly labels are typically not available. Autoencoder is a very powerful method for the unsupervised learning of high-level data representations. Recent development in deep learning has endowed autoencoders with even greater capability in analyzing complex, high-dimensional and large-scale data. This study investigates the potential of autoencoders in detecting anomalies in Building energy data. An autoencoder-based ensemble method is proposed while providing a comprehensive comparison on different autoencoder types and training schemes. Considering the unique learning mechanism of autoencoders, specific methods have been designed to evaluate the autoencoder performance. The research results can be used as foundation for Building professionals to develop advanced tools for anomaly detection and performance benchmarking.
-
Unsupervised data analytics in mining big Building operational data for energy efficiency enhancement: A review
Energy and Buildings, 2018Co-Authors: Cheng Fan, Fu Xiao, Jiayuan WangAbstract:Abstract Building Operations account for the largest proportion of energy use throughout the Building life cycle. The energy saving potential is considerable taking into account the existence of a wide variety of Building operation deficiencies. The advancement in information technologies has made modern Buildings to be not only energy-intensive, but also information-intensive. Massive amounts of Building operational data, which are in essence the reflection of actual Building operating conditions, are available for knowledge discovery. It is very promising to extract potentially useful insights from big Building operational data, based on which actionable measures for energy efficiency enhancement are devised. Data mining is an advanced technology for analyzing big data. It consists of two main types of data analytics, i.e., supervised and unsupervised analytics. Despite of the power of supervised analytics in predictive modeling, unsupervised analytics are more practical and promising in discovering novel knowledge given limited prior knowledge. This paper provides a comprehensive review on the current utilization of unsupervised data analytics in mining massive Building operational data. The commonly used unsupervised analytics are summarized according to their knowledge representations and applications. The challenges and opportunities are elaborated as guidance for future research in this multi-disciplinary field.
-
Temporal knowledge discovery in big BAS data for Building energy management
Energy and Buildings, 2015Co-Authors: Cheng Fan, Henrik Madsen, Fu Xiao, Dan WangAbstract:With the advances of information technologies, today's Building automation systems (BASs) are capable of managing Building operational performance in an efficient and convenient way. Meanwhile, the amount of real-time monitoring and control data in BASs grows continually in the Building lifecycle, which stimulates an intense demand for powerful big data analysis tools in BASs. Existing big data analytics adopted in the Building automation industry focus on mining cross-sectional relationships, whereas the temporal relationships, i.e., the relationships over time, are usually overlooked. However, Building Operations are typically dynamic and BAS data are essentially multivariate time series data. This paper presents a time series data mining methodology for temporal knowledge discovery in big BAS data. A number of time series data mining techniques are explored and carefully assembled, including the Symbolic Aggregate approXimation (SAX), motif discovery, and temporal association rule mining. This study also develops two methods for the efficient post-processing of knowledge discovered. The methodology has been applied to analyze the BAS data retrieved from a real Building. The temporal knowledge discovered is valuable to identify dynamics, patterns and anomalies in Building Operations, derive temporal association rules within and between subsystems, assess Building system performance and spot opportunities in energy conservation.
Jiayuan Wang - One of the best experts on this subject based on the ideXlab platform.
-
discovering gradual patterns in Building Operations for improving Building energy efficiency
Applied Energy, 2018Co-Authors: Cheng Fan, Fu Xiao, Yongjun Sun, Kui Shan, Jiayuan WangAbstract:Abstract The development of information technologies has enabled real-time monitoring and controls over Building Operations. Massive amounts of Building operational data are being collected and available for knowledge discovery. Advanced data analytics are urgently needed to fully realize the potentials of big Building operational data in enhancing Building energy efficiency. The rapid development of data mining has provided powerful tools for extracting insights in various knowledge representations. Gradual pattern mining is a promising technique for discovering useful patterns from Building operational data. The knowledge discovered is represented as gradual relationships, i.e., “the more/less A, the more/less B”. It can bring special interests to Building energy management by highlighting co-variations among numerical Building variables. This study investigated the usefulness of gradual pattern mining for Building energy management. A generic methodology was proposed to ensure the quality and applicability of the knowledge discovered. The methodology was validated through a case study. The results showed that the methodology could successfully extract valuable insights on Building operation characteristics and provide opportunities for Building energy efficiency enhancement.
-
analytical investigation of autoencoder based methods for unsupervised anomaly detection in Building energy data
Applied Energy, 2018Co-Authors: Cheng Fan, Fu Xiao, Yang Zhao, Jiayuan WangAbstract:Abstract Practical Building Operations usually deviate from the designed Building operational performance due to the wide existence of operating faults and improper control strategies. Great energy saving potential can be realized if inefficient or faulty Operations are detected and amended in time. The vast amounts of Building operational data collected by the Building Automation System have made it feasible to develop data-driven approaches to anomaly detection. Compared with supervised analytics, unsupervised anomaly detection is more practical in analyzing real-world Building operational data, as anomaly labels are typically not available. Autoencoder is a very powerful method for the unsupervised learning of high-level data representations. Recent development in deep learning has endowed autoencoders with even greater capability in analyzing complex, high-dimensional and large-scale data. This study investigates the potential of autoencoders in detecting anomalies in Building energy data. An autoencoder-based ensemble method is proposed while providing a comprehensive comparison on different autoencoder types and training schemes. Considering the unique learning mechanism of autoencoders, specific methods have been designed to evaluate the autoencoder performance. The research results can be used as foundation for Building professionals to develop advanced tools for anomaly detection and performance benchmarking.
-
Unsupervised data analytics in mining big Building operational data for energy efficiency enhancement: A review
Energy and Buildings, 2018Co-Authors: Cheng Fan, Fu Xiao, Jiayuan WangAbstract:Abstract Building Operations account for the largest proportion of energy use throughout the Building life cycle. The energy saving potential is considerable taking into account the existence of a wide variety of Building operation deficiencies. The advancement in information technologies has made modern Buildings to be not only energy-intensive, but also information-intensive. Massive amounts of Building operational data, which are in essence the reflection of actual Building operating conditions, are available for knowledge discovery. It is very promising to extract potentially useful insights from big Building operational data, based on which actionable measures for energy efficiency enhancement are devised. Data mining is an advanced technology for analyzing big data. It consists of two main types of data analytics, i.e., supervised and unsupervised analytics. Despite of the power of supervised analytics in predictive modeling, unsupervised analytics are more practical and promising in discovering novel knowledge given limited prior knowledge. This paper provides a comprehensive review on the current utilization of unsupervised data analytics in mining massive Building operational data. The commonly used unsupervised analytics are summarized according to their knowledge representations and applications. The challenges and opportunities are elaborated as guidance for future research in this multi-disciplinary field.
