Automated recognition and mapping of building management system (BMS) data points for building energy modeling (BEM)

With the advance of the internet of things and building management system(BMS)in modern buildings,there is an opportunity of using the data to extend the use of building energy modeling(BEM)beyond the design phase.Potential applications include retrofit analysis,measurement and verification,and operations and controls.However,while BMS is collecting a vast amount of operation data,different suppliers and sensor installers typically apply their own customized or even random non-uniform rules to define the metadata,i.e.,the point tags.This results in a need to interpret and manually map any BMS data before using it for energy analysis.The mapping process is labor-intensive,error-prone,and requires comprehensive prior knowledge.Additionally,BMS metadata typically has considerable variety and limited context information,limiting the applicability of existing interpreting methods.In this paper,we proposed a text mining framework to facilitate interpreting and mapping BMS points to EnergyPlus variables.The framework is based on unsupervised density-based clustering(DBSCAN)and a novel fuzzy string matching algorithm“X-gram”.Therefore,it is generalizable among different buildings and naming conventions.We compare the proposed framework against commonly used baselines that include morphological analysis and widely used text mining techniques.Using two building cases from Singapore and two from the United States,we demonstrated that the framework outperformed baseline methods by 25.5%,with the measurement extraction F-measure of 87.2%and an average mapping accuracy of 91.4%.

Field synergy analysis of pollutant dispersion in street canyons and its optimization by adding wind catchers

The microenvironment,which involves pollutant dispersion of the urban street canyon,is critical to the health of pedestrians and residents.The objectives of this work are twofold:(i)to effectively assess the pollutant dispersion process based on a theory and(ii)to adopt an appropriate stratigy,i.e.,wind catcher,to alleviate the pollution in the street canyons.Pollutant dispersion in street canyons is essentially a convective mass transfer process.Because the convective heat transfer process and the mass transfer process are physically similar and the applicability of field synergy theory to turbulence has been verified in the literature,we apply the field synergy theory to the study of pollutant dispersion in street canyons.In this paper,a computational fluid dynamics(CFD)simulation is conducted to investigate the effects of wind catcher,wind speed and the geometry of the street canyons on pollutant dispersion.According to the field synergy theory,Sherwood number and field synergy number are used to quantitatively evaluate the wind catcher and wind speed on the diffusion of pollutants in asymmetric street canyons.The results show that adding wind catchers can significantly improve the air quality of the step-down street canyon and reduce the average pollutant concentrations in the street canyon by 75%.Higher wind speed enhances diffusion of pollutants differently in different geometric street canyons.

Architectural quality of the productive facades integrating photovoltaic and vertical farming systems: Survey among experts in Singapore

Buildings could play a critical role in energy and food production while making highdensity cities more resilient.Productive facades(PFs),as flexible and multi-functional systems integrating photovoltaic(PV)and vertical farming(VF)systems,could contribute to transforming buildings and communities from consumers to producers.This study analyses the architectural quality of the developed PF concept drawing on the findings of a web-survey conducted among experts e building professionals in Singapore.The developed design variants are compared with regards to key design aspects such as facade aesthetics,view from the inside,materialisation,ease of operation,functionality and overall architectural quality.The study also compares and discusses the results of the web-survey with the results of a previously conducted door-to-door survey among the potential users-residents of the Housing&Development Board(HDB)blocks.The findings confirm an overall acceptance of the PF concept and reveal a need for synergetic collaboration between architects/designers and other building professionals.Based on the defined PF design framework and the results of the two surveys,a series of recommendations and improved PF prototypes are proposed for further assessment and implementation in order to foster their scalability from buildings into communities and cities.

Trends and driving forces of low-carbon energy technology innovation in China’s industrial sectors from 1998 to 2017: from a regional perspective

Low-carbon energy technology(LC)innovation contributes to both environmental protection and economic development.Using the panel data of 30 provinces/autonomous regions/municipalities in China from 1998 to 2017,this paper constructs a two-layer logarithmic mean Divisia index(LMDI)model to uncover the factors influencing the variation of the innovation of LC in China’s industrial sectors,including the alternative energy production technology(AEPT)and the energy conversation technology(ECT).The results show that China’s industrial LC patent applications rapidly increased after 2005 and AEPT patent applications outweighed ECT patent applications all the time with a gradually narrowing gap.Low-carbon degree played the dominant role in promoting the increase in China’s industrial LC patent applications,followed by the economic scale,R&D(research and development)efficiency,and R&D share.Economic structure contributed to the increases in LC patent applications in the central and the western regions,while led to the decreases in the eastern region,the north-eastern region,and Chinese mainland.Low-carbon degree and economic scale were two main contributors to the growths of both industrial AEPT patent applications and ECT patent applications in Chinese mainland and the four regions.Several policy recommendations are made to further promote industrial innovation in China.

Physics-based,data-driven approach for predicting natural ventilation of residential high-rise buildings

Natural ventilation is particularly important for residential high-rise buildings as it maintains indoor human comfort without incurring the energy demands that air-conditioning does.To improve a building’s natural ventilation,it is essential to develop models to understand the relationship between wind flow characteristics and the building's design.Significantly more effort is still needed for developing such reliable,accurate,and computationally economical models instead of currently the most popular physics-based models such as computational fluid dynamics(CFD)simulation.This paper,therefore,presents a novel model developed based on physics-based modelling and a data-driven approach to evaluate natural ventilation in residential high-rise buildings.The model first uses CFD to simulate wind pressures on the exterior surfaces of a high-rise building.Once the surface pressures have been obtained,multizone modelling is used to predict the air change per hour(ACH)for different flats in various configurations.Data-driven prediction models are then developed using data from the simulation and deep neural networks that are based on mean absolute error,mean absolute percentage error,and a fusion algorithm respectively.These data-driven models are used to predict the ACH of 25 flats.The results from multizone modelling and data-driven modelling are compared.The results imply a high accuracy of the data-driven prediction in comparison with physics-based models.The fusion algorithm-based neural network performs best,achieving 96%accuracy,which is the highest of all models tested.This study contributes a more efficient and robust method for predicting wind-induced natural ventilation.The findings describe the relationship between building design(e.g.,plan layout),distribution of surface pressure,and the resulting ACH,which serve to improve the practical design of sustainable buildings.

A simplified tool for building layout design based on thermal comfort simulations

Thermal comfort aspects of indoor spaces are crucial during the design stages of building layout planning. This study presents a simplified tool based on thermal comfort using predicted mean vote （PMV） index. Thermal comfort simulations were performed for 14 different possible room Layouts based on window configurations. ECOTECT 12 was used to determine the PMV of these rooms for one full year, leading to 17,808 simulations. Simulations were performed for three different climatic zones in India and were validated using in-situ measurements from one of these climatic zones. For moderate climates, rooms with window openings on the south facade exhibited the best thermal comfort conditions for nights, with comfort conditions prevailing for approximately 79.25% of the time annually. For operation during the day, windows on the north facade are favored, with thermal comfort conditions prevailing for approximately 77.74% of the time annually. Similar results for day and night time operation for other two climatic zones are presented. Such an output is essential in deciding the layout of buildings on the basis of functionality of the different rooms （living room, bedroom, kitchen） corresponding to different operation times of the day.

Value creation of road infrastructure networks:A structural equation approach

Although road agencies need to provide road infrastructure that is beneficial for road users, little is known about how the activities of the agencies influence the value creation of road infrastructure. From a service-dominant logic perspective, the importance of road main- tenance and traffic management activities for the contribution of road infrastructure to the value-creation process of road users is investigated. Road agencies facilitate the value creation of road users by maintaining, upgrading or renewing road infrastructure, the provision of information about the current traffic situation, possible redirection routes in case of traffic jams, and suggestions for appropriate driving behavior. Based on a structured questionnaire, data were collected among motorists in Singapore and analyzed by means of a partial least square modeling approach. The analysis revealed that road cleanliness and road evenness have a significant effect on the experience of road maintenance. Important and significant indicators for the experience of traffic management are the clarity of road signs and the efficiency of traffic redirection. A main conclusion of the research is that for traffic-intensive networks, both road maintenance and traffic man- agement activities are important contributors to the value creation of road infrastructure with a slightly stronger contribution of traffic management activities. Road agencies need to find appropriate maintenance strategies which reduce and coordinate simultaneous maintenance interventions on the network to such an extent that traffic management activities are able to minimize any considerable loss of traffic flow.

Urban heat island research： Challenges and potential

With the increasing urbanization, studies have shown that the Urban Heat Istand （UHI） Intensity in many cities has increased drastically. This issue is becoming even more critical because of the imminent effect of Climate Change. Many cities are experiencing extreme heatwaves more frequently. It is essential that more research should be conducted to understand the impact of UHI and also to examine the relationship between UHI and Climate Change.

Statics of space syntax:Analysis of daylighting

Space syntax involves a set of techniques for analyzing the spatial configurations of various spaces at building and urban scales.Religious spaces,such as prayer halls,are exam ples of buildings where observers experience space mostly from a single point of view.Furthermore,traditional space syntax is mainly used in the analysis of visibility and space cognition in terms of isovists and graph-based measures.The other aspects of space cognition,such as day lighting,artificial lighting,and glare,are carried in the isolation of the space syntax analysis.This paper proposes the scaling of the space syntax field for the inclusion of other parameters,such as daylighting,and integration of the associated performative measures to space syntax analysis of the mosque typology to aid in the studying of overall space cognition based on com fort and environmental parameters.We present a case study on a typical mosque layout using m ulti-objective optim ization.The analysis presented in the paper has im plications for the architectural designs of spaces with respect to glare m anagem ent and daylight potential.Moreover,it is unique and builds on our previous work for the exploration of comfort,visibility,and proximity thresholds for stationary observers.

A review on 5G technology for smart energy management and smart buildings in Singapore

Sustainable and smart building is a recent concept that is gaining momentum in public opinion,and thus,it is making its way into the agendas of researchers and city authorities all over the world.To move towards sustainable development goals,5G technology would make significant impacts are building construction,operation,and management by facilitating high-class services,providing efficient functionalities.It’s well known that the Singapore is one of top smart cities in this world and from the first counties that adopted of 5G technology in various sectors including smart buildings.Based on these facts,this paper discusses the international trends in 5G applications for smart buildings,and R&D and test bedding works conducted in 5G labs.As well as,the manuscript widely reviewed and discussed the 5G technology development,use cases,applications and future projects which supported by Singapore government.Finally,the 5G use cases for smart buildings and build environment improvement application were discussed.This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data.

Building occupancy and energy consumption:Case studies across building types

Past research has shown that occupancy information can be used to reduce building energy consumption through occupant-based controls and by mitigating wasteful occupant behavior.In this study,we investigate the dynamic relationship between WiFi connection counts(as a proxy to occupancy)and building electricity consumption across four building typologies(office,lab,health center,and library).Our findings based on one year of data show a strong positive linear correlation between electricity consumption and WiFi count across all four building when the building is in operation.The data exploration also indicates higher interactions between occupants with the plug and lighting loads in office and lab space types as compared to in a health center and a library.Next,using principal component analysis(PCA)for feature extraction followed by Density-based spatial clustering of applications with noise(DBSCAN),we show that distinct clusters could be generated,characterized by an increase in the between-cluster variance and smaller within-cluster variation.Lastly,we apply linear regression to manifest how the clustering results can be used to better model the variables.

Special report on the fourth international conference on countermeasures to urban heat island （4th IC2UHI Conference） in Singapore

Initiated by Prof. Hashem Akbari, international conference on countermeasures to urban heat island （IC2UHI Conference） was first held in Tokyo, Japan in year 2006. Subsequently, it was held in Berkeley, USA and Venice, Italy in year 2009 and 2014 respectively.

Probabilistic graphical models in energy systems:A review

Probabilistic graphical models(PGMs)can effectively deal with the problems of energy consumption and occupancy prediction,fault detection and diagnosis,reliability analysis,and optimization in energy systems.Compared with the black-box models,PGMs show advantages in model interpretability,scalability and reliability.They have great potential to realize the true artificial intelligence in energy systems of the next generation.This paper intends to provide a comprehensive review of the PGM-based approaches published in the last decades.It reveals the advantages,limitations and potential future research directions of the PGM-based approaches for energy systems.Two types of PGMs are summarized in this review,including static models(SPGMs)and dynamic models(DPGMs).SPGMs can conduct probabilistic inference based on incomplete,uncertain or even conflicting information.SPGM-based approaches are proposed to deal with various management tasks in energy systems.They show outstanding performance in fault detection and diagnosis of energy systems.DPGMs can represent a dynamic and stochastic process by describing how its state changes with time.DPGM-based approaches have high accuracy in predicting the energy consumption,occupancy and failures of energy systems.In the future,a unified framework is suggested to fuse the knowledge-driven and data-driven PGMs for achieving better performances.Universal PGM-based approaches are needed that can be adapted to various energy systems.Hybrid algorithms would outperform the basic PGMs by integrating advanced techniques such as deep learning and first-order logic.

Analyzing construction safety through time series methods

The construction industry produces a large amount of data on a daily basis. However, existing data sets have not been fully exploited in analyzing the safety factors of construction projects. Thus, this work describes how temporal analysis techniques can be applied to improve the safety management of construction data. Various time series (TS) methods were adopted for identifying the leading indicators or predictors of construction accidents. The data set used herein was obtained from a large construction company that is based in Singapore and contains safety inspection scores, accident cases, and project-related data collected from 2008 to 2015. Five projects with complete and sufficient data for temporal analysis were selected from the data set. The filtered data set contained 23 potential leading indicators (predictors or input variables) of accidents (output or dependent variable). TS analyses were used to identify suitable accident predictors for each of the five projects. Subsequently, the selected input variables were used to develop three different TS models for predicting accident occurrences, and the vector error correction model was found to be the best model. It had the lowest root mean squared error value for three of the five projects analyzed. This study provides insights into how construction companies can utilize TS data analysis to identify projects with high risk of accidents.

Reconstructing the Residential Wards in Tang Period Chang’an Based on a Theoretical Ward Categorization System

长安是唐帝国的首都(公元618-906年)。历史与考古证据的不足,制约了对长安街区特征的深入研究。在最近的研究中,我们按照某些参数对街区进行分类,以构建更易于理解的框架。这些参数为形式、规模和内容。这种系统研究法提供了一个理解街区物理形式和结构的框架。它也为街区中城市景观的重建和想象提供了基础。论文选取三个典型街区进行数字重建。按照文献记载,这些街区当时依照主人的社会地位填充着不同类型和规模的院子。重建三个选区的这种系统方法.适用于长安的所有街区.它为我们提供了一个理论城市模型,可用作讨论和交换关于长安新研究的平台。