eQUEST Based Building Energy Modeling Analysis for Energy Efficiency of Buildings

Building energy performance is a function of numerous building parameters.In this study,sensitivity analysis on twenty parameters is performed to determine the top three parameters that have the most significant impact on the energy performance of buildings.Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in the Quick Energy Simulation Tool(eQUEST).The model is calibrated using the Normalized Mean Bias Error(NMBE)and Coefficient of Variation of Root Mean Square Error(CV(RMSE))method.The model satisfies the NMBE and CV(RMSE)criteria set by the American Society of Heating,Refrigeration,and Air-Conditioning(ASHRAE)Guideline 14,Federal Energy Management Program(FEMP),and International Performance Measurement and Verification Protocol(IPMVP)for building energy model calibration.The values of the parameters are varied in two levels,and then the percentage change in output is calculated.Fractional factorial analysis on eight parameters with the highest percentage change in energy performance is performed at two levels in a statistical software JMP.For building A,the top 3 parameters from the percentage change method are:Heating setpoint,cooling setpoint and server room.From fractional factorial design,the top 3 parameters are:heating setpoint(p-value=0.00129),cooling setpoint(p-value=0.00133),and setback control(p-value=0.00317).For building B,the top 3 parameters from both methods are:Server room(pvalue=0.0000),heating setpoint(p-value=0.00014),and cooling setpoint(p-value=0.00035).If the best values for all top three parameters are taken simultaneously,energy efficiency improves by 29%for building A and 35%for building B.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Integrating Virtual Reality and Energy Analysis with BIM to Optimize Window-to-Wall Ratio and Building’s Orientation for Age-in-Place Design at the Conceptual Stage

This study unfolds an innovative approach aiming to address the critical role of building design in global energy consumption, focusing on optimizing the Window-to-Wall Ratio (WWR), since buildings account for approximately 30% of total energy consumed worldwide. The greatest contributors to energy expenditure in buildings are internal artificial lighting and heating and cooling systems. The WWR, determined by the proportion of the building’s glazed area to its wall area, is a significant factor influencing energy efficiency and minimizing energy load. This study introduces the development of a semi-automated computer model designed to offer a real-time, interactive simulation environment, fostering improving communication and engagement between designers and owners. The said model serves to optimize both the WWR and building orientation to align with occupants’ needs and expectations, subsequently reducing annual energy consumption and enhancing the overall building energy performance. The integrated model incorporates Building Information Modeling (BIM), Virtual Reality (VR), and Energy Analysis tools deployed at the conceptual design stage, allowing for the amalgamation of owners’ inputs in the design process and facilitating the creation of more realistic and effective design strategies.

Energy-Efficient Approaches for a Machine Tool Building in a University through Field Measurement and Energy Modelling

The heating,ventilating,and air conditioning(HVAC)system consumes nearly 50%of the building’s energy,especially in Taiwan with a hot and humid climate.Due to the challenges in obtaining energy sources and the negative impacts of excessive energy use on the environment,it is essential to employ an energy-efficient HVAC system.This study conducted the machine tools building in a university.The field measurement was carried out,and the data were used to conduct energymodelling with EnergyPlus(EP)in order to discover some improvements in energy-efficient design.The validation between fieldmeasurement and energymodelling was performed,and the error rate was less than 10%.The following strategies were proposed in this study based on several energy-efficient approaches,including room temperature settings,chilled water supply temperature settings,chiller coefficient of performance(COP),shading,and building location.Energy-efficient approaches have been evaluated and could reduce energy consumption annually.The results reveal that the proposed energy-efficient approaches of room temperature settings(3.8%),chilled water supply temperature settings(2.1%),chiller COP(5.9%),using shading(9.1%),and building location(3.0%),respectively,could reduce energy consumption.The analysis discovered that using a well-performing HVAC system and building shading were effective in lowering the amount of energy used,and the energy modelling method could be an effective and satisfactory tool in determining potential energy savings.

Analysis and Modeling of the Central Air-Conditioning System in Intelligent Buildings

The central air conditioning system in an intelligent building (IB) was analyzed and modeled in order to perform the optimization scheduling strategy of the central air conditioning system. A set of models proposed and a type of periodically autoregressive model (PAR) based on the improved genetic algorithms (IGA) were used to perform the optimum energy saving scheduling. The example of the Liangmahe Plaza was taken to show the effectiveness of the methods.

Photovoltaic energy balance estimation based on the building integration level

The photovoltaic module building integration level affects the module temperature and,consequently,its output power.In this work,a methodology has been proposed to estimate the influence of the level of architectural photovoltaic integration on the photovoltaic energy balance with natural ventilation or with forced cooling systems.The developed methodology is applied for five photovoltaic module technologies(m⁃Si,p⁃Si,a⁃Si,CdTe,and CIGS)on four characteristic locations(Athens,Davos,Stockholm,and Würzburg).To this end,a photovoltaic module thermal radiation parameter,PVj,is introduced in the characterization of the PV module technology,rendering the correlations suitable for building⁃integrated photovoltaic(BIPV)applications,with natural ventilation or with forced cooling systems.The results show that PVj has a significant influence on the energy balances,according to the architectural photovoltaic integration and climatic conditions.Keywords:Photovoltaic cooling;BIPV;Photovoltaic;Ventilation;Photovoltaic integration level in building【OA】(2)Graph⁃Based methodology for Multi⁃Scale generation of energy analysis models from IFC,by Asier Mediavilla,Peru Elguezabal,Natalia Lasarte,Article 112795 Abstract:Process digitalisation and automation is unstoppable in all industries,including construction.However,its widespread adoption,even for non⁃experts,demands easy⁃to⁃use tools that reduce technical requirements.BIM to BEM(Building Energy Models)workflows are a clear example,where ad⁃hoc prepared models are needed.This paper describes a methodology,based on graph techniques,to automate it by highly reducing the input BIM requirements found in similar approaches,being applicable to almost any IFC.This is especially relevant in retrofitting,where reality capture tools(e.g.,3D laser scanning,object recognition in drawings)are prone to create geometry clashes and other inconsistencies,posing higher challenges for automation.Another innovation presented is its multi⁃scale nature,efficiently addressing the surroundings impact in the energy model.The application to selected test cases has been successful and further tests are ongoing,considering a higher variety of BIM models in relation to tools and techniques used and model sizes.

Impact of mechanical ventilation control strategies based on non-steady-state and steady-state Wells-Riley models on airborne transmission and building energy consumption

Ventilation is an effective solution for improving indoor air quality and reducing airborne transmission.Buildings need sufficient ventilation to maintain a low infection risk but also need to avoid an excessive ventilation rate,which may lead to high energy consumption.The Wells-Riley(WR)model is widely used to predict infection risk and control the ventilation rate.However,few studies compared the non-steady-state(NSS)and steady-state(SS)WR models that are used for ventilation control.To fill in this research gap,this study investigates the effects of the mechanical ventilation control strategies based on NSS/SS WR models on the required ventilation rates to prevent airborne transmission and related energy consumption.The modified NSS/SS WR models were proposed by considering many parameters that were ignored before,such as the initial quantum concentration.Based on the NSS/SS WR models,two new ventilation control strategies were proposed.A real building in Canada is used as the case study.The results indicate that under a high initial quantum concentration(e.g.,0.3 q/m^(3))and no protective measures,SS WR control underestimates the required ventilation rate.The ventilation energy consumption of NSS control is up to 2.5 times as high as that of the SS control.

End-to-end data-driven modeling framework for automated and trustworthy short-term building energy load forecasting

Conventional automated machine learning(AutoML)technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments,leading to accuracy reduction in forecasting short-term building energy loads.Moreover,their predictions are not transparent because of their black box nature.Hence,the building field currently lacks an AutoML framework capable of data quality enhancement,environment self-adaptation,and model interpretation.To address this research gap,an improved AutoML-based end-to-end data-driven modeling framework is proposed.Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data.It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers.A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation,contributing to the accuracy enhancement of AutoML technologies.Moreover,a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework.It overcomes the poor interpretability of conventional AutoML technologies.The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building.It is discovered that the accuracy of the improved framework increases by 4.24%–8.79%compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data.Furthermore,it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework.The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.

Building Information Modeling for Environmental Impact Assessment in Early Design Phases: A Literature Review

The building sector is the largest consumer of energy in industrial countries. Saving energy in new buildings or building renovations can thus lead to significant global environmental impacts. In this endeavor, building information modeling (BIM) and building energy modeling (BEM) are two important tools to make the transition to net-zero energy buildings (NZEB). So far, little attention has been devoted, in the literature, to discuss the connection between BIM, BEM, and Life-cycle assessment (LCA), which is the main topic of this article. A literature review of 157 journal articles and conference proceedings published between 1990 and 2020 is presented. This review outlines knowledge gaps concerning BIM, BEM, and environmental impact assessment. It suggests that defining the process with the right technology (at the right time) would result in a more integrated design process (IDP) and bridge current gaps. The most efficient way to improve process and technology is related to the competences of the architects, engineers and constructors (AEC). The review also indicates that the IDP in the early design phases (EDP) is in need of improvement for architects and engineers, where a better connection between design phases, specific levels of development (LOD) and BIM tools is needed. Competences, process and technology are the three main themes addressed in the review. Their relation to design phases and LOD is discussed. The aim is to propose possible solutions to the current hinders in BIM-to-BEM (BIM2BEM) and BIM-for-LCA (BIM4LCA) integration.

Analysis and forecast of residential building energy consumption in Chongqing on carbon emissions

Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.

Evaluation of building energy demand forecast models using multi-attribute decision making approach

With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).

A new database of building-space-specific internal loads and load schedules for performance based code compliance modeling of commercial buildings

Building-level loads and load schedules prescribed by current modeling rules save modelers time and provide standards during whole building performance modeling.However,recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load schedules for different space types.As a solution to this issue,this paper presents a database of default building-space-specific loads and load schedules for use in energy modeling,and in particular code compliance modeling for commercial buildings.The existing sets of default loads and load schedules are reviewed and the challenges behind using them for specific research topics are discussed.Then,the proposed method to develop the building-space-specific loads and load schedules is introduced.After that,the database for these building-space-specific loads and load schedules is presented.In addition,one case is studied to demonstrate the applications of these loads and load schedules.In this case study,three methods are used to develop building energy models:space-specific(using knowledge of the distribution and location of space types and applying the space-specific data in the developed database),building-level(assuming a lack of knowledge of the space types and using the building-level data in the developed database),and calculated-ratio(assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database).The energy results simulated by using these three methods are compared,which shows building-level methods can produce significantly different absolute energy and energy savings results than the results using space-specific methods.Finally,this paper discusses the application scope and maintenance of this new database.

The potential of remote sensing and GIS in urban building energy modelling

As the world continues to urbanize at an unprecedented rate,the energy demand in cities is rising.Buildings account for over 75%of all the energy consumed in cities and are responsible for over two-thirds of the emissions.Assessment of energy demand in buildings is a highly integrative endeavour,bringing together the interdisciplinary fields of energy and urban studies,along with a host of technical domains namely,geography,engineering,economics,sociology,and planning.In the last decade,several urban building energy modelling tools(UBEMs)have been developed for estimation as well as prediction of energy demand in cities.These models are useful in policymaking as they can evaluate future urban energy scenarios.However,data acquisition for generating the input database for UBEM has been a major challenge.In this review,a comprehensive assessment of the potential of remote sensing and GIS techniques for UBEM has been presented.Firstly,the most common input variables of UBEM have been identified by reviewing recent publications on UBEM and then studies related to the acquisition of data corresponding to these variables have been explored.More than 140 research papers and review articles relevant to remote sensing and GIS applications for building level data extraction in urban areas and UBEM applications have been investigated for this purpose.After going through level of details required for each of the input components of UBEM and studying the possibility of acquiring some of those data using remote sensing,it has been inferred that satellite remote sensing and Unmanned Aerial Vehicles(UAVs)have a strong potential in enhancing the input data space for UBEM but their applicability has been limited.Further,the challenges of the usage of these technologies and the possible solutions have also been presented in this study.It is recommended to utilise the existing methodologies of extracting information from remote sensing and GIS for UBEM,along with newer techniques such as machine learning and artificial intelligence.

Optimizing building retrofit through data analytics:A study of multi-objective optimization and surrogate models derived from energy performance certificates

The building stock is responsible for a large share of global energy consumption and greenhouse gas emissions,therefore,it is critical to promote building retrofit to achieve the proposed carbon and energy neutrality goals.One of the policies implemented in recent years was the Energy Performance Certificate(EPC)policy,which proposes building stock benchmarking to identify buildings that require rehabilitation.However,research shows that these mechanisms fail to engage stakeholders in the retrofit process because it is widely seen as a mandatory and complex bureaucracy.This study makes use of an EPC database to integrate machine learning techniques with multi-objective optimization and develop an interface capable of(1)predicting a building’s,or household’s,energy needs;and(2)providing the user with optimum retrofit solutions,costs,and return on investment.The goal is to provide an open-source,easy-to-use interface that guides the user in the building retrofit process.The energy and EPC prediction models show a coefficient of determination(R2)of 0.84 and 0.79,and the optimization results for one case study EPC with a 2000€budget limit inÉvora,Portugal,show decreases of up to 60%in energy needs and return on investments of up to 7 in 3 years.

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities:Current trends and future outlook

Energy demand fluctuations due to low probability high impact(LPHI)micro-climatic events such as urban heat island effect(UHI)and heatwaves,pose significant challenges for urban infrastructure,particularly within urban built-clusters.Mapping short term load forecasting(STLF)of buildings in urban micro-climatic setting(UMS)is obscured by the complex interplay of surrounding morphology,micro-climate and inter-building energy dynamics.Conventional urban building energy modelling(UBEM)approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale.Reduced order modelling,unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization,limit the inter-building energy dynamics consideration into UBEMs.In addition,mismatch of resolutions of spatio-temporal datasets(meso to micro scale transition),LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations.This review aims to direct attention towards an integrated-UBEM(i-UBEM)framework to capture the building load fluctuation over multi-scale spatio–temporal scenario.It highlights usage of emerging data-driven hybrid approaches,after systematically analysing developments and limitations of recent physical,data-driven artificial intelligence and machine learning(AI-ML)based modelling approaches.It also discusses the potential integration of google earth engine(GEE)-cloud computing platform in UBEM input organization step to(i)map the land surface temperature(LST)data(quantitative attribute implying LPHI event occurrence),(ii)manage and pre-process high-resolution spatio-temporal UBEM input-datasets.Further the potential of digital twin,central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored.It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

Identifying the validity domain of machine learning models in building energy systems

The building sector significantly contributes to climate change.To improve its carbon footprint,applications like model predictive control and predictive maintenance rely on system models.However,the high modeling effort hinders practical application.Machine learning models can significantly reduce this modeling effort.To ensure a machine learning model’s reliability in all operating states,it is essential to know its validity domain.Operating states outside the validity domain might lead to extrapolation,resulting in unpredictable behavior.This paper addresses the challenge of identifying extrapolation in data-driven building energy system models and aims to raise knowledge about it.For that,a novel approach is proposed that calibrates novelty detection algorithms towards the machine learning model.Suitable novelty detection algorithms are identified through a literature review and a benchmark test with 15 candidates.A subset of five algorithms is then evaluated on building energy systems.First,on two-dimensional data,displaying the results with a novel visualization scheme.Then on more complex multi-dimensional use cases.The methodology performs well,and the validity domain could be approximated.The visualization allows for a profound analysis and an improved understanding of the fundamental effects behind a machine learning model’s validity domain and the extrapolation regimes.

Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank as active Thermal Energy Storage (TES), where two optimization problems are integrated together to optimize both the ASHP electricity consumption and the building heating consumption utilizing a heat dynamic model of the building. The results show that the proposed EMPC can save the energy cost by load shifting compared with some reference cases.

Climate change and building ageing impact on building energy performance and mitigation measures application:A case study in Turin, northern Italy

This study uses a building energy performance simulation to investigate the impact of predicted climate warming and the additional issue of building ageing on the energy performance for a library in Turin,Italy.The climate and ageing factors were modelled individually and then integrated together for several decades.Results from the climate-only simulation showed a decrease in thebuilding heating energy usage which outweighed the increase in the on-site cooling energy demand occurring in a warming scenario.The study revealed a high sensitivity of energy performance to building ageing,in particular due to HVAC(Heating,Ventilation and Air Conditioning) equipment efficiency degradation.Building ageing was seen to negatively affect the energy performance as it induced a further increase of the cooling energy usage in a warming climate,while it also counteracted the reduction of the heating energy usage resulting from warming.Simulations on the combination of mitigation techniques showed a number of potentially retrofit measures that would be beneficial for buildings to avoid an increase in the cooling energy usage due to climate warming.The combination of these retrofit techniques showed a potential decrease of 87.3% in the final cooling energy usage for the considered building.

Sensibility Analysis of the Brazilian Standards for Energy Efficiency Regarding the Variation of Internal Load Density in Office Buildings in Brazilian Bioclimatic Zones 1 and 7

The RTQ-C （Technical Requirements of Quality for the Energy Performance Level of Commercial Buildings） publication classified the buildings in five efficiency levels. In RTQ-C, the evaluation can be done with two methods： a prescriptive method and a simulation one. This paper aims to identify the sensibility of the prescriptive method RTQ-C regarding the variation of equipment internal load density in office buildings in bioclimatic Zones I and 7 of the Brazilian bioclimatic zoning. The research results show that the building with walls and roof configured to meet specific prerequisites for energy efficiency Levels B and C had a lower consumption than buildings that meet the prerequisites to Level A. The study also showed that buildings with high internal load density of equipment, maximum shape factor and high, with walls and roofs with higher thermal transmittance, have lower power consumption than constructions with an envelope with greater thermal resistance. The increase in internal load density causes an increase in the internal heat generated by the large amount of equipment. In buildings with higher thermal insulation （Level A）, the internal heat is maintained in the environment, causing overheating and the need for an air conditioning system.

Thermal Bridges Impact on Energy Consumption in Residential Buildings Considering the Brazilian Bioclimatic Zoning

With the considerable increase in electric power consumption, searching for buildings with lower energy impact has become a crucial factor on controlling energy consumption, as well as designing buildings with high thermal comfort. Thermal bridges are weak points in buildings where the thermal resistance varies considerably between two distinct points. Depending on the situation, the existence of thermal bridges in a building can be favorable to the achievement of the expected thermal comfort and lower energy consumption. The aim of this paper is to analyze the impact of thermal bridges of reinforced concrete structure regarding to energy consumption for residential buildings in the Brazilian bioclimatic zones. The used method is characterized by computer simulations of distinct cases configured with and without thermal bridges. The results show that in most bioclimatic zones, the presence of thermal bridges in the wall composition contributes to the reduction of energy consumption for both heating and cooling, and independent of the wall＇s insulation level, solar absorptance is a major factor in the energy consumption levels, walls with smaller absorptance consume less and this consumption increases gradually with increasing absorptance.