On the applicability of various levels of detail for occupant behavior representation and modeling in building performance simulation

Occupant behavior(OB)is one of the significant sources of uncertainty in building performance simulation.While OB modeling has received increased attention in the past decade,research on the degree of granularity or level of detail(LoD)required for representing occupants is still in the nascent stages.This paper analyzes the modeling and applicability of three LoDs to represent occupants in building performance assessment.A medium-sized prototype office building located in Chicago,Illinois is used as the simulation case study.Ten occupant-centric attributes are adopted to develop the LoDs for OB representation.We first demonstrate the different modeling approaches required for simulating the three fidelity levels.Later,we illustrate the suitability of the developed LoDs in supporting six building performance use cases across different lifecycle stages.This study intends to provide guidance for the building simulation community on appropriate OB representation to support various use cases.

The application of building performance simulation in the writing of architectural history： Analysing climatic design in 1960s Israel

This article presents a methodology for the integration of building performance simulation （BPS） into the writing of architectural history. While BPS tools have been developed mainly for design purposes, their current maturity enables to reliably apply them in simulating the performance of past buildings, even when these buildings have been significantly modified or demolished. The possibility to virtually reconstruct the performance of past buildings can help us to overcome the existing knowledge gap in the understanding of the role played by building performance and building performance research through the history of architecture and can therefore promote the intelligent and successful application of environmental features in contemporary architecture. The potential of the proposed methodology is presented here using a historical case study from 1960s Israel （a university building in Tel Aviv）, in which climatic considerations were an explicit part of the entire design process. The original thermal performance of the building was analysed by employing the EnergyPlus simulation engine, and the simulation results were used for evaluating the climatic impact of certain design decisions, comparing them with the proclaimed design goals and the original intentions of the architects.

Development and Application of Decision Support Model for the Performance Optimization of Office Buildings Based on Grasshopper

With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there is a lack of high⁃performance building tools and the workflow is often time⁃consuming.The building performance simulation,multiple objective optimizations,and the decision support model are the new approaches of high⁃performance building design.This paper proposes a newly developed decision support model,a high⁃performance building decision model named HPBuildingDSM,which integrates the building performance simulation,building performance multiple objective optimizations,building performance sampling,and parameter sensitivity analysis to design high⁃performance office buildings.In this research,the HPBuildingDSM was operated to search for the desirable office building design results with low⁃energy and high⁃quality daylighting performances.The simulated results had better daylighting performance and lower energy consumption,whose UDI100-2000 was 37.94%and annual energy consumption performance was 76.28 kWh/(m2·a),indicating a better building performance than the optimized results in the previous case study.

GPPre:A Python⁃Based Tool in Grasshopper for Office Building Performance Optimization

With the development of the economic and low⁃carbon society,high⁃performance building(HPB)design plays an increasingly important role in the architectural area.The performance of buildings usually includes the building energy consumption,building interior natural daylighting,building surface solar radiation,and so on.Building performance simulation(BPS)and multiple objective optimizations(MOO)are becoming the main methods for obtaining a high performance building in the design process.Correspondingly,the BPS and MOO are based on the parametric tools,like Grasshopper and Dynamo.However,these tools are lacking the data analysis module for designers to select the high⁃performance building more conveniently.This paper proposes a toolkit“GPPre”developed based on the Grasshopper platform and Python language.At the end of this paper,a case study was conducted to verify the function of GPPre,which shows that the combination of the sensitivity analysis(SA)and MOO module in the GPPre could aid architects to design the buildings with better performance.

Plus Energy Buildings:A Numerical Case Study

The feasibility of Plus Energy Building for a sample relevant case is investigated.After a literature review aimed to identify key aspects of this type of buildings,a preliminary evaluation of the thermal performance of a building constructed using conventional material is presented together with a parametric analysis of the impact of typical influential parameters.Solar domestic hot water(SDHW)and photovoltaic systems(PV)are considered in the study.Numerical simulations indicate that for the examined sample case(Beirut in Lebanon)the total annual energy need of conventional building is 87.1 kWh/y.m^(2).About 49%of energy savings can be achieved by improving the building envelope and installing energy efficient technologies.Moreover,about 90%of energy savings in domestic hot water production can be achieved by installing a SDHW system composed of two solar collectors connected in series.Finally,the addition of a grid connected PV array system can significantly mitigate the energy needs of the building leading to an annual excess of energy.

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.

Residential building performance analysis at near extreme weather conditions in Hong Kong through a thermal-comfort-based strategy

The precise building performance assessment of residential housings in subtropical regions is usually more difficult than that for the commercial premises due to the much more complicated behavior of the occupants with regard to the change in indoor temperature.The conventional use of a fixed schedule for window opening,clothing insulation and cooling equipment operation cannot reflect the real situation when the occupants respond to the change in thermal comfort,thus affecting the appropriateness of the assessment results.To rectify the situation,a new modeling strategy in which the modification of the various operation schedules was based on the calculated thermal comfort(TC),was developed in this study.With this new TC-based strategy,the realistic building performances under different cooling provision scenarios applied to a high-rise residential building under the near extreme weather conditions were investigated and compared.It was found that sole provision of ventilation fans could not meet the zone thermal comfort by over 68%of the time,and air-conditioning was essential.The optimal use of ventilation fans for cooling could only help reduce the total cooling energy demand by less than 12%at best which could only be realistically evaluated by adopting the present strategy.Parametric studies were conducted which revealed that some design factors could offer opportunities for reducing the total cooling energy under the near extreme weather conditions.

Investigating the use cases of a novel heat battery in Dutch residential buildings

Recent advances in thermochemical storage technology have introduced a novel closed-loop thermal energy storage(TES)system,known as the heat battery(HB),which is believed to have great potential for aiding the energy transition in the built environment because of its higher energy density and neglectable storage loss compared to conventional TES systems.In order to investigate the potential use cases of the HB and provide practical feedback for its further development,this research employs a simulation-based approach to analyze its influence on building performance in various use cases within Dutch residential buildings.Stakeholders including the homeowner,distribution system operator,and district heating system operator are identified,and a preliminary list of use cases is defined based on relevant literature and input from the HB developer.The simulation approach is conducted to predict key performance indicators for each stakeholder.The Kruskal-Wallis test was employed to sort and scrutinize the simulation outcomes and discern the significance of each use case element.The findings demonstrated that the HB holds the potential to diminish both the operational energy cost by up to 30%for the homeowners and the peak heating load transmitted from the building to the district heating system.

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

Building performance simulation has been adopted to support decision making in the building life cycle.An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations.Building energy model calibration is a technique that takes various types of measured performance data(e.g.,energy use)and tunes key model parameters to match the simulated results with the actual measurements.This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings.A public building dataset that includes high-level building attributes(e.g.,building type,vintage,total floor area,number of stories,zip code)of 111 buildings in San Francisco,California,USA,was used to generate building models in EnergyPlus.Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’monthly electricity and natural gas consumption.The results showed 57 out of 111 buildings were successfully calibrated against actual buildings,while the remaining buildings showed opportunities for future calibration improvements.Enhancements to the pattern-based model calibration method are identified to expand its use for:(1)central heating,ventilation and air conditioning(HVAC)systems with chillers,(2)space heating and hot water heating with electricity sources,(3)mixed-use building types,and(4)partially occupied buildings.

Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models

Fast machine learning-based surrogate models are trained to emulate slow,high-fidelity engineering simulation models to accelerate engineering design tasks.This introduces uncertainty as the surrogate is only an approxi-mation of the original model.Bayesian methods can quantify that uncertainty,and deep learning models exist that follow the Bayesian paradigm.These models,namely Bayesian neural networks and Gaussian process models,enable us to give predic-tions together with an estimate of the model’s uncertainty.As a result we can derive uncertainty-aware surrogate models that can automatically identify unseen design samples that may cause large emulation errors.For these samples the high-fidelity model can be queried instead.This paper outlines how the Bayesian paradigm allows us to hybridize fast but approximate and slow but accurate models.In this paper,we train two types of Bayesian models,dropout neural networks and stochastic variational Gaussian Process models,to emulate a complex high dimensional building energy performance simulation problem.The surrogate model processes 35 building design parameters(inputs)to estimate 12 annual building energy perfor-mance metrics(outputs).We benchmark both approaches,prove their accuracy to be competitive,and show that errors can be reduced by up to 30%when the 10%of samples with the highest uncertainty are transferred to the high-fidelity model.

CoolVox:Advanced 3D convolutional neural network models for predicting solar radiation on building facades

Data-driven models have become increasingly prominent in the building,architecture,and construction industries.One area ideally suited to exploit this powerful new technology is building performance simulation.Physics-based models have traditionally been used to estimate the energy flow,air movement,and heat balance of buildings.However,physics-based models require many assumptions,significant computational power,and a considerable amount of time to output predictions.Artificial neural networks(ANNs)with prefabricated or simulated data are likely to be a more feasible option for environmental analysis conducted by designers during the early design phase.Because ANNs require fewer inputs and shorter computation times and offer superior performance and potential for data augmentation,they have received increased attention for predicting the surface solar radiation on buildings.Furthermore,ANNs can provide innovative and quick design solutions,enabling designers to receive instantaneous feedback on the effects of a proposed change to a building's design.This research introduces deep learning methods as a means of simulating the annual radiation intensities and exposure level of buildings without the need for physics-based engines.We propose the CoolVox model to demonstrate the feasibility of using 3D convolutional neural networks to predict the surface radiation on building facades.The CoolVox model accurately predicted the radiation intensities of building facades under different boundary conditions and performed better than ARINet(with average mean square errors of 0.01 and 0.036,respectively)in predicting the radiation intensity both with(validation error=0.0165)and without(validation error=0.0066)the presence of boundary buildings.