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.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization.Urban building energy modeling(UBEM)is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations,supporting the implementation of carbon emission reduction policies.Currently,most studies focus on the energy performance of archetype buildings under climate change,which is hard to obtain refined results for individual buildings when scaling up to an urban area.Therefore,this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas,by taking two urban neighborhoods comprising 483 buildings in Geneva,Switzerland as case studies.In this regard,GIS datasets and Swiss building norms were collected to develop an archetype library.The building heating energy consumption was calculated by the UBEM tool—AutoBPS,which was then calibrated against annual metered data.A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%.The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).The results showed a decrease of 22%–31%and 21%–29%for heating energy consumption,an increase of 113%–173%and 95%–144%for cooling energy consumption in the two neighborhoods by 2050.The average annual heating intensity dropped from 81 kWh/m^(2) in the current typical climate to 57 kWh/m^(2) in the SSP5-8.5,while the cooling intensity rose from 12 kWh/m^(2) to 32 kWh/m^(2).The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7%and 18.6%,respectively,in the SSP scenarios.The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets

Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential.This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable.A case study was conducted for 68,966 buildings in Changsha city,China.First,clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets.Then,the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years.The year built of residential buildings was collected from the housing website.Moreover,twenty-two building types and three vintages were selected as archetype buildings to represent 59,332 buildings,covering 87.4%of the total floor area.Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings.Finally,monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus.The total electricity and natural gas use for the 59,332 buildings was 13,864 GWh and 23.6×10^(6) GJ.Three energy conservation measures were evaluated to demonstrate urban energy saving potential.The proposed methods can be easily applied to other cities in China.

Extracting typical occupancy schedules from social media (TOSSM) and its integration with building energy modeling

Building occupancy,one of the most important consequences of occupant behaviors,is a driving influencer for building energy consumption and has been receiving increasing attention in the building energy modeling community.With the vast development of information technologies in the era of the internet-of-things,occupant sensing and data acquisition are not limited to a single node or traditional approaches.The prevalence of social networks provides a myriad of publically available social media data that might contain occupancy information in the space for a given time.In this paper,we explore two approaches to extract the typical occupancy schedules for the input to the building energy simulation based on the data from social networks.The first approach uses text classification algorithms to identify whether people are present in the space where they are posting on social media.On top of that,the typical building occupancy schedules are extracted with assumed people counting rules.The second approach utilizes the processed Global Positioning System(GPS)tracking data provided by social networking service companies such as Facebook and Google Maps.Web scraping techniques are used to obtain and post-process the raw data to extract the typical building occupancy schedules.The results show that the extracted building occupancy schedules from different data sources(Twitter,Facebook,and Google Maps)share a similar trend but are slightly distinct from each other and hence may require further validation and corrections.To further demonstrate the application of the extracted Typical Occupancy Schedules from Social Media(TOSSM),data-driven models for predicting hourly energy usage prediction of a university museum are developed with the integration of TOSSM.The results indicate that the incorporation of TOSSM could improve the hourly energy usage prediction accuracy to a small extent regarding the four adopted evaluation metrics for this museum building.

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%.

Simulating dispatchable grid services provided by flexible building loads:State of the art and needed building energy modeling improvements

End-use electrical loads in residential and commercial buildings are evolving into flexible and cost-effective resources to improve electric grid reliability,reduce costs,and support increased hosting of distributed renewable generation.This article reviews the simulation of utility services delivered by buildings for the purpose of electric grid operational modeling.We consider services delivered to(1)the high-voitage bulk power system through the coordinated action of many,distributed building loads working together,and(2)targeted support provided to the operation of low-voltage electric distribution grids.Although an exhaustive exploration is not possible,we emphasize the ancillary services and voltage management buildings can provide and summarize the gaps in our ability to simulate them with traditional building energy modeling(BEM)tools,suggesting pathways for future research and development.

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.

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.

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.

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.

Evaluation of Energy Efficiency Performance of Heated Windows

Fenestration systems are widely used across the world.There is expansive research on window configurations,frames,and glazing technology,but not enough research has been published on reducing window heat loss through heat application to a pane.The presented study attempted to evaluate the performance of heated windows by developing an experimental setup to test a window at various temperatures by varying the power input to thewindow.Heated double pane window was installed in an insulated box.Atemperature gradient was developed across the window by cooling one side of the window using gel-based ice packs.The other face of the window was heated by enabling power at different wattages through the window.The temperature of the inside and outside panes,current and voltage input,and temperature of the room and box were recorded.The data was used to calculate the apparent effective resistance of the window when not being heated vs.when being heated.The study concluded that,when window temperature was maintained close to the room temperature,the heated double pane window is effective in reducing heat loss by as much as 50%as compared to a non-heated double pane window.When temperature of the window was much higher than the room temperature,the heat loss through the window increased beyond that of a non-heated window.The issues encountered during the current stages of experiments are noted,and recommendations are provided for future studies.

Energy modeling and data structure framework for Sustainable Human-Building Ecosystems （SHBE）- a review

This paper contributes an inclusive review of scientific studies in the field of sustainable human building ecosystems （SHBEs）. Reducing energy consumption by making buildings more energy efficient has been touted as an easily attainable approach to promoting carbon-neutral energy societies. Yet, despite significant progress in research and technology development, for new buildings, as energy codes are getting more stringent, more and more technologies, e.g., LED lighting, VRF systems, smart plugs, occupancy-based controls, are used. Nevertheless, the adoption of energy efficient measures in buildings is still limited in the larger context of the developing countries and middle income/low-income population. The objective of Sustainable Human Building Ecosystem Research Coordination Network （SHBE-RCN） is to expand synergistic investigative podium in order to subdue barriers in engineering, architectural design, social and economic perspectives that hinder wider application, adoption and subsequent performance of sustainable building solutions by recognizing the essential role of human behaviors within building-scale ecosystems. Expected long-term outcomes of SHBE-RCN are collaborative ideas for transformative technologies, designs and methods of adoption for future design, construction and operation of sustainable buildings.

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.

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.

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.

A CAD-BEM geometry transformation method for face-based primary geometric input based on closed contour recognition

Performance analysis during the early design stage can significantly reduce building energy consumption.However,it is difficult to transform computer-aided design(CAD)models into building energy models(BEM)to optimize building performance.The model structures for CAD and BEM are divergent.In this study,geometry transformation methods was implemented in BES tools for the early design stage,including auto space generation(ASG)method based on closed contour recognition(CCR)and space boundary topology calculation method.The program is developed based on modeling tools SketchUp to support the CAD format(like*.stl,*.dwg,*.ifc,etc.).It transforms face-based geometric information into a zone-based tree structure model that meets the geometric requirements of a single-zone BES combined with the other thermal parameter inputs of the elements.In addition,this study provided a space topology calculation method based on a single-zone BEM output.The program was developed based on the SketchUp modeling tool to support additional CAD formats(such as*.stl,*.dwg,*.ifc),which can then be imported and transformed into*.obj.Compared to current methods mostly focused on BIM-BEM transformation,this method can ensure more modeling flexibility.The method was integrated into a performance analysis tool termed MOOSAS and compared with the current version of the transformation program.They were tested on a dataset comprising 36 conceptual models without partitions and six real cases with detailed partitions.It ensures a transformation rate of two times in any bad model condition and costs only 1/5 of the time required to calculate each room compared to the previous version.

Daily power demand prediction for buildings at a large scale using a hybrid of physics-based model and generative adversarial network

Power demand prediction for buildings at a large scale is required for power grid operation.The bottom-up prediction method using physics-based models is popular,but has some limitations such as a heavy workload on model creation and long computing time.Top-down methods based on data driven models are fast,but less accurate.Considering the similarity of power demand patterns of single buildings and the superiority of generative adversarial network(GAN),this paper proposes a new method(E-GAN),which combines a physics-based model(EnergyPlus)and a data-driven model(GAN),to predict the daily power demand for buildings at a large scale.The new E-GAN method selects a small number of typical buildings and utilizes EnergyPlus models to predict their power demands.Utilizing the prediction for those typical buildings,the GAN then is adopted to forecast the power demands of a large number of buildings.To verify the proposed method,the E-GAN is used to predict 24-hour power demands for a set of residential buildings.The results show that(1)4.3%of physics-based models in each building category are required to ensure the prediction accuracy;(2)compared with the physics-based model,the E-GAN can predict power demand accurately with only 5%error(measured by mean absolute percentage error,MAPE)while using only approximately 9%of the computing time;and(3)compared with data-driven models(e.g.,support vector regression,extreme learning machine,and polynomial regression model),E-GAN demonstrates at least 60%reduction in prediction error measured by MAPE.

AutoBPS-BIM:A toolkit to transfer BIM to BEM for load calculation and chiller design optimization

This study developed a rapid building modeling tool,AutoBPS-BIM,to transfer the building information model(BIM)to the building energy model(BEM)for load calculation and chiller design optimization.An eight-storey office building in Beijing,33.2 m high,67.2 m long and 50.4 m wide,was selected as a case study building.First,a module was developed to transfer BIM in IFC format into BEM in EnergyPlus.Variable air volume systems were selected for the air system,while water-cooled chillers and boilers were used for the central plant.The EnergyPlus model calculated the heating and cooling loads for each space as well as the energy consumption of the central plant.Moreover,a chiller optimization module was developed to select the optimal chiller design for minimizing energy consumption while maintaining thermal comfort.Fifteen available chillers were included,with capacities ranging from 471 kW to 1329 kW.The results showed that the cooling loads of the spaces ranged from 33 to 100 W/m^(2) with a median of 45 W/m^(2),and the heating load ranged from 37 to 70 W/m^(2) with a median of 52 W/m^(2).The central plant’s total cooling load under variable air volume systems was 1400 kW.Compared with the static load calculation method,the dynamic method reduced 33%of the chiller design capacity.When two chillers were used,different chiller combinations’annual cooling energy consumption ranged from 10.41 to 11.88,averaging 11.12 kWh/m^(2).The lowest energy consumption was 10.41 kWh/m^(2) when two chillers with 538 kW and 1076 kW each were selected.Selecting the proper chiller number with different capacities was critical to achieving lower energy consumption,which achieved 12.6%cooling system energy consumption reduction for the case study building.This study demonstrated that AutoBPS-BIM has a large potential in modeling BEM and optimizing chiller design.

The comparison of design airflow rates with dynamic and steady-state displacement models in varied dynamic conditions

A temperature-based method is usually applied in displacement ventilation (DV) design when overheating is the primary indoor climate concern. Different steady-state models have been developed and implemented to calculate airflow rate in rooms with DV. However, in practical applications, the performance of DV depends on potentially dynamic parameters, such as strength, type and location of heat gains and changing heat gain schedule. In addition, thermal mass affects dynamically changing room air temperature. The selected steady-state and dynamic models were validated with the experimental results of a lecture room and an orchestra rehearsal room. Among the presented models, dynamic DV model demonstrated a capability to take into account the combination of dynamic parameters in typical applications of DV. The design airflow rate is calculated for the case studies of dynamic DV design in the modelled lecture room in both dynamic and steady-state conditions. In dynamic conditions of heavy construction in 2–4 hours occupancy periods, the actual airflow rate required could be 50% lower than the airflow rate calculated with the steady-state models. The difference between steady-state and dynamic multi-nodal model is most significant with heavyweight construction and short occupancy period (17%–28%). In cases with light construction, the dynamic DV model provides roughly the same airflow rates for four-hour occupancy period than the Mund’s model calculates. The dynamic model can significantly decrease the design airflow rate of DV, which can result in a reduction of investment costs and electrical consumption of fans.