Quantifying the Impact of Transportation Network Companies on Urban Congestion in a Medium Sized City

In the recent years,TNCs(transportation network companies)and on-demand ridesharing services have grown rapidly.Given conflicting reports on TNC impacts,a need exists to study mode choice shifts in the presence of TNC services and their effects on urban congestion.Using Birmingham,AL(Alabama)as a case study,this paper showcases the feasibility of modeling TNC services using the MATSim(Multi-Agent Transport Simulation)platform,and evaluating the impact of such services on traffic operations.Data used for the study were gathered from Uber drivers and riders through surveys,as well as the US Census.The results indicate that when 200,400,and 800 TNC vehicles are added to the network,the VKT(vehicle kilometers traveled)increase by 22%,23.6%,and 23.2%,respectively,compared to the baseline scenario(no TNC service).Analysis of hourly average speeds,hourly average travel times,and hourly volumes along study corridors further indicate that TNC services increase traffic congestion,in particular,during the AM/PM peak periods.Moreover,the study shows that the optimal TNC fleet size for the Birmingham region is 400 to 500 active TNC vehicles per day.Such fleet size minimizes idle time and the number of TNC vehicles hovering,which have adverse impacts on TNC drivers,and the environment while ensuring TNC service availability and reasonable waiting times for TNC customers.

Potential Benefits of Increased Public Transit Ridership in Medium Sized Cities: A Case Study

At the beginning of the twentieth century, the United States was leading in the public transit sector, but following World War II, private automobiles became more affordable and gained popularity. Transportation infrastructure investments that increased road capacity further facilitated the increase in automobile use at the expense of reduced public transit ridership. With the increase of dependency on automobiles and the continuing growth of private automobile ownership and use, various problems became major challenges in big cities of USA. These include traffic congestion, air pollution, road and parking infrastructure costs, energy consumption, traffic safety, fewer mobility options for the non-drivers, and a decline in the image and use of public transit. This study uses a medium sized city, Birmingham as a case study to investigate the potential of public transit to reduce automobile trips and in turn improve the overall performance of the road network by addressing the abovementioned challenges. An agent-based simulation model was developed for the Birmingham metropolitan region using the Multi-agent Transport Simulation (MATSim) platform. Three scenarios were considered with gradually increased transit ridership to identify the benefits of increased public transit. Traffic volume, network average speed, and travel times were used as performance measures for the evaluation of the designated scenarios. Results suggest that modal shifts toward public transit and reduction in travel demand for an automobile can result in improvements in speed and travel time for all users. Therefore, investments for improving transit quality and frequency of service, as well as campaigns to improve the image of public transit and make it a mode of choice for transportation users can increase transit ridership and, in turn, improve network operations, thus are deemed worthy for medium sized cities.

A review of validation methods for building energy modeling programs

Building energy simulation analysis plays an important supporting role in the conservation of building energy.Since the early 1980s,researchers have focused on the development and validation of building energy modeling programs(BEMPs)and have basically formed a set of systematic validation methods for BEMPs,mainly including analytical,comparative,and empirical methods.Based on related papers in this field,this study systematically analyzed the application status of validation methods for BEMPs from three aspects,namely,sources of validation cases,comparison parameters,and evaluation indicators.The applicability and characteristics of the three methods in different validation fields and different development stages of BEMPs were summarized.Guidance were proposed for researchers to choose more suitable validation methods and evaluation indicators.In addition,the current development trend of BEMPs and the challenges faced by validation methods were investigated,as well as the existing progress of current validation methods under this trend was analyzed.Subsequently,the development direction of the validation method was clarified.

A global typical meteorological year(TMY)database on ERA5 dataset

The outdoor climate condition is one of the deterministic factors influencing building energy consumption.Building performance simulation(BPS)tools usually adopt typical meteorological year(TMY)as the outdoor climate input.Despite that many scholars and institutes have developed TMY datasets,these datasets are usually based on distinct data sources and methods.Considering the increase of international cooperation construction projects,compatible TMY dataset for different countries is in urgent need.This paper presents a global typical meteorological year(TMY)database covering 38,947 stations worldwide based on the fifth-generation atmospheric reanalysis product released by the European Center(ERA5).The data is created with Chinese Standard Weather Database(CSWD)method to reflect the average level of historical weather.The dataset is saved in a 55 GB database of compressed CSV files and a website is established where users can download their required TMY data for certain cities according to the longitude and latitude information.A systematic validation is conducted to confirm the feasibility of ERA5 as data source and validity of generated TMY data.This TMY-ERA5 dataset is fundamental and essential in building system designs of international construction projects,building performance simulation,especially for some countries lacking ground meteorological stations or missing meteorological year data in the building sector.It can be used as references for other meteorological climate studies.

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

Buildings have a significant impact on global sustainability.During the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance.Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications.Recent advances in information technologies and data science have enabled convenient access,storage,and analysis of massive on-site measurements,bringing about a new big-data-driven research paradigm.This paper presents a critical review of data-driven methods,particularly those methods based on larger datasets,for building energy modeling and their practical applications for improving building performances.This paper is organized based on the four essential phases of big-data-driven modeling,i.e.,data preprocessing,model development,knowledge post-processing,and practical applications throughout the building lifecycle.Typical data analysis and application methods have been summarized and compared at each stage,based upon which in-depth discussions and future research directions have been presented.This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling.Furthermore,considering the ever-increasing development of smart buildings and IoT-driven smart cities,the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

Challenges and opportunities for carbon neutrality in China’s building sector—Modelling and data

The building sector is one of the largest energy user and carbon emitter globally.To achieve China’s national carbon target,the building sector in China needs to achieve carbon peaking and neutrality targets by 2030 and 2060,respectively.However,data deficiency on building energy and emissions become barriers for tracking the status of building energy and emissions,and identify potential opportunities for achieving dual carbon targets.To address these shortcomings,this study established an integrated China Building Energy and Emission Model(CBEEM).With CBEEM,this study evaluated the building-construction and building-operation energy and emissions in China,and revealed the status quo and potential challenge and opportunities.According to modelling results,building operation energy use of China was 1.06 billion tce in 2020,accounting for 21%of China’s total primary energy consumption.Building construction energy consumption was 0.52 billion tce in 2020,accounting for another 10%of total primary energy consumption.Key messages found on building carbon emissions are:building construction embodied emissions were 1.5 billion tCO_(2) in 2020 and are declining slowly,building operational carbon emissions were 2.2 billion tCO_(2) in 2020 and are still increasing.International comparisons between China and other countries on building stock,energy use intensity and carbon emission intensity were conducted as well,and help shed a light on the challenges for decarbonization of China’s building sector.Finally,technology perspectives to achieve carbon neutrality target were discussed and related policy suggestions were provided.

A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development

The lighting system accounts for 8%of the total electricity consumption in commercial buildings in the United States and 12%of the total electricity consumption in public buildings globally.This consumption share can be effectively reduced using the demand-response control.The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared(PIR)sensor.However,the detection inaccuracy of the PIR sensor usually results in false-offs.To diminish the false-error frequency,the existing lighting system control simply deploys a delayed reaction period(e.g.,5 to 20 min),which is not sufficiently accurate for the demand-response operation.Therefore,in this research,a novel data-driven model predictive control(MPC)method that is based on the temporal sequential-based artificial neural network(TS-ANN)is proposed to overcome this challenge using an updated historical occupancy status.Using an office as case study,the proposed model is also compared with the traditional lighting system control method.In the proposed model,the occupancy data was trained to predict the occupancy pattern to improve the control.It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature.The simulation results indicated that the proposed method achieved higher accuracy(97.4%)and fewer false-offs(from 79.5 with traditional time delay method to 0.6 times per day)are achieved by the MPC model.The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation.

Advanced data analytics for building energy modeling and management

The data-driven approach has been widely adopted in fault detection and diagnosis and optimal control of building energy systems for improving building performance since the 1980s,owing to the increasing demands for convenient and computationally efficient yet accurate models for online and automated applications.

Chinese prototype building models for simulating the energy performance of the nationwide building stock

Building energy modeling(BEM)has become increasingly used in building energy conservation research.Prototype building models are developed to represent the typical urban building characteristics of a specific building type,meteorological conditions,and construction year.This study included four residential buildings and 11 commercial buildings to represent nationwide building types in China.With consideration of five climate zones and different construction years corresponding to national standards,a total of 151 prototype building models were developed.The building envelope properties,occupancy and energy-related behaviors,and heating,ventilation,and air-conditioning(HVAC)system characteristics were defined according to the corresponding building energy efficiency design standards,HVAC design standards,and through other sources,such as questionnaire surveys,on-site measurements,and literature,which reflect the real situation of existing buildings in China.Based on the developed prototype buildings,a large database of 9225 models in 270 cities was further developed to facilitate users to simulate building energy in different cities.In conclusion,the developed prototype building models can represent realistic building characteristics and construction practices of the most common residential and commercial buildings in China,serving as an important foundation for BEM.The models can be used for analyses related to building energy conservation research on typical individual buildings,including energy-saving technologies,advanced controls,and new policies,and providing a reference for the development of building energy codes and standards.

A novel AC turning on behavior model based on survival analysis

Occupant control behavior is a key factor affecting the energy consumption of building air-conditioners(ACs).The operating behavior of ACs and their models in office buildings have been investigated extensively.However,although the thermal sensation of occupants is affected by their previous thermal experience,few researchers have attempted to incorporate this effect quantitatively in models of AC turning on behavior.Not considering the cumulative effect may result in inaccurate predictions.Therefore,in this study,a survival model is proposed to describe AC turning on behavior in office buildings under the cumulative dimension of time.Based on a dataset containing environmental parameters and occupant behavior information,as well as considering occupants entering a room as the starting event and turning on an air-conditioner as the end event,the endurance time before an AC is turned on is investigated,and a survival model is used to predict the probability of the AC turning on due to environmental factors.Based on a switch curve,confusion matrix,and tolerance–time curve,the prediction results of the survival model are analyzed and validated.The results show that a tolerance temperature of 29℃and a tolerance duration setting of 1 h can effectively model the turning on behavior of the AC.In addition,based on comparison results of different models,the survival model presents a more stable switching curve,a higher F1 score,and a tolerance curve that is more similar to reality.Different tolerance durations,as well as static and dynamic tolerance temperature settings,are considered to optimize the model.Furthermore,the AC energy consumption is calculated under the survival model and the traditional Weibull model.Simulation results were compared with measurement,and the survival model verified the improvement effect of prediction accuracy by 8%than the Weibull model.By considering the time-transformed accumulation of physical environmental factors,the accuracy of AC turning on models can be improved,thus providing an effective reference for future building energy consumption simulations.

An action-based Markov chain modeling approach for predicting the window operating behavior in office spaces

Reliable energy and performance prediction for building design and planning is important for newly-designed or retrofitted buildings.Window operating behavior has an important influence on the ventilation and energy consumption of these buildings under different realistic scenarios.Therefore,quantitatively describing this behavior and constructing a prediction model are important.In this work,an action-based Markov chain modeling approach for predicting window operating behavior in office spaces was proposed.Two summer measurement data(2016 and 2018)were used to verify the accuracy and validity of the modeling approach.The opening rate,outdoor temperature,time distribution,and on-off curve were proposed as four inspection standards.This study also compared the prediction performance between the action-based Markov chain modeling approach with the state-based Markov chain modeling approach,which is the most popular modeling approach to model occupant window operating behavior.This study proved that the yearly variation of occupants’behavior performed a form of action that remained unchanged during a certain period.Meanwhile,the results also proved that the action-based Markov chain modeling approach can reflect the actual window operating behavior accurately within an open-plan office,which is a beneficial supplement for energy-consumption simulation software in a window-state prediction module.The state-based Markov chain modeling approach showed better stability and accuracy in terms of the opening rate,whereas the action-based Markov chain modeling approach showed good consistency with the measurement data in the on-off curves and in situations with little data.For the on-off curves,the accuracy of action-based modeling approach in the prediction of window open-state is 20%higher.

DeST 3.0:A new-generation building performance simulation platform

Buildings contribute to almost 30%of total energy consumption worldwide.Developing building energy modeling programs is of great significance for lifecycle building performance assessment and optimization.Advances in novel building technologies,the requirements of high-performance computation,and the demands for multi-objective models have brought new challenges for building energy modeling software and platforms.To meet the increasing simulation demands,DeST 3.0,a new-generation building performance simulation platform,was developed and released.The structure of DeST 3.0 incorporates four simulation engines,including building analysis and simulation(BAS)engine,HVAC system engine,combined plant simulation(CPS)engine,and energy system(ES)engine,connected by air loop and water loop balancing iterations.DeST 3.0 offers numerous new simulation features,such as advanced simulation modules for building envelopes,occupant behavior and energy systems,cross-platform and compatible simulation kernel,FMI/FMU-based co-simulation functionalities,and high-performance parallel simulation architecture.DeST 3.0 has been thoroughly evaluated and validated using code verification,inter-program comparison,and case-study calibration.DeST 3.0 has been applied in various aspects throughout the building lifecycle,supporting building design,operation,retrofit analysis,code appliance,technology adaptability evaluation as well as research and education.The new generation building simulation platform DeST 3.0 provides an efficient tool and comprehensive simulation platform for lifecycle building performance analysis and optimization.

Machine learning and evolutionary prediction of superhard BC-N compounds

We build random forests models to predict elastic properties and mechanical hardness of a compound,using only its chemical formula as input.The model training uses over 10,000 target compounds and 60 features based on stoichiometric attributes,elemental properties,orbital occupations,and ionic bonding levels.Using the models,we construct triangular graphs for B-C-N compounds to map out their bulk and shear moduli,as well as hardness values.The graphs indicate that a 1:1 B-N ratio can lead to various superhard compositions.We also validate the machine learning results by evolutionary structure prediction and density functional theory.Our study shows that BC_(10)N,B_(4)C_(5)N_(3),and B_(2)C_(3)N exhibit dynamically stable phases with hardness values>40 GPa,which are superhard materials that potentially could be synthesized by low-temperature plasma methods.

Clustering-based probability distribution model for monthly residential building electricity consumption analysis

Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades.One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households.Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately.To fill this gap,this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model.The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns.Based on the clustering results,probability distributions are fitted for all feature parameters within each sub-cluster.The model is then validated with three validation approaches.Monthly electricity consumption in households of the Jiangsu Province,China,was studied to test the performance of the model.Lastly,this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features.

Comparative research on different air conditioning systems for residential buildings

Two types of air conditioning （AC） systems generally exist, namely, centralized and decen- tralized AC systems. This study focuses on three actual engineering projects of residentiat communities where centralized AC systems are adopted. The applicability of centralized AC systems in residential buildings is discussed and analyzed. In addition, the key elements that lead to different building energy consumptions and system efficiencies between centralized and decentralized AC systems in residential buildings are investigated. This study shows that in residential buildings, at the point where the centralized feature of the system meets the decentralized feature of users＇ load, the problems of high energy consumption and low energy efficiency could easily occur.

Building a greener future—Progress of the green building technology in the “13th Five-Year Plan” of China

The building sector plays an important role in energyconservation and carbon neutrality in China.Accordingto the Building Energy Research Center of TsinghuaUniversity,carbon dioxide(CO_(2))emissions account for38%of the total emissions,of which 16%and 22%arecontributed by building construction and operation,respectively(Hu et al.2022),Hence,promoting greenbuildings is proposed as an effective solution to the keyproblems of building industry development,including lowlabor productivity,high consumption of building materials,and high COz emissions in construction and operation.Greenbuildings are environmentlly responsible and resource-efficient throughout their life cycle from siting,design,andconstruction to operation,maintenance,renovation,anddeconstruction.They aim to use energy,water,and otherresources ficiently,while protecting occupant health andimproving productivity,adopting eco-friendly materials,and reducing the production of waste and pollution.

Building Simulation to be published monthly from 2022

It is my honor to be the Editor-in-Chief of Building Simulation,succeeding Professor Xudong Yang,the founding Editor-in-Chief.The Journal is undergoing rapid growth in many ways.To meet the increasing demand from both our readers and authors,the Journal has decided to change the publishing frequency from bi-monthly to monthly,effective Issue 1,Vol.15(2022).We will now publish approximately 120 articles annually.

Comparative analysis of window operating behavior in three different open-plan offices in Nanjing

Research on the window operating behavior of offices is of great significance for reducing building energy con-sumption and improving indoor comfort.The open-plan office is a common office form that involves a large number of people and a complex staffcomposition.The window operating behaviors in open-plan offices are also random and various.This study took three open-plan offices with different situations(area,office type,staffcomposition,etc.)as an example,which provides a new perspective on how people behave differently when opening or closing windows.The window operating behaviors in two typical seasons(summer and transition seasons)were recorded and analyzed.The occupants’schedules and influencing factors of window operating behavior were investigated by questionnaire surveys.In addition,the indoor environmental parameters,occu-pancy situation,and on-offstatuses of windows and air conditioning were acquired through field measurements.Furthermore,the differences in window operating behaviors in the three open-plan offices were compared from the perspectives of influencing factors,duration of the window on-offstatuses,and cause of window control ac-tions,among others.In addition,Spearman Correlation Coefficient was used to analyze the ranks of the candidate motivations for window operating behaviors.The preliminary results show that influenced by the personnel com-position,type of air conditioner and adjustable degree of windows,the window operating behaviors of different office buildings have larger discrepancies than that in the same building.However,there were some common characteristics in the window regulation behaviors of the three open-plan offices:they were generally influenced by the coupling of environmental factors,schedule factors,and equipment factors.This study reveals that when expand the research object from a single building to multiple buildings,more difficulties and challenges would be involved into behavior research.