Energy-Efficient and Cost-Effective Approaches through Energy Modeling for Hotel Building

Hotel buildings are currently among the largest energy consumers in the world.Heating,ventilation,and air conditioning are the most energy-intensive building systems,accounting for more than half of total energy consumption.An energy audit is used to predict the weak points of a building’s energy use system.Various factors influence building energy consumption,which can be modified to achieve more energy-efficient strategies.In this study,an existing hotel building in Central Taiwan is evaluated by simulating several scenarios using energy modeling over a year.Energy modeling is conducted by using Autodesk Revit 2025.It was discovered from the results that arranging the lighting schedule based on the ASHRAE Standard 90.1 could save up to 8.22%of energy consumption.And then the results also revealed that changing the glazing of the building into double-layer lowemissivity glass could reduce energy consumption by 14.58%.While the energy consumption of the building could also be decreased to 7.20%by changing the building orientation to the north.Meanwhile,moving the building location to Northern Taiwan could also minimize the energy consumption of the building by 3.23%.The results revealed that the double layer offers better thermal insulation,and low-emissivity glass can lower energy consumption,electricity costs,and CO_(2)emissions by up to 15.27%annually.While adjusting orientation and location can enhance energy performance,this approach is impractical for existing buildings,but this could be considered for designing new buildings.The results showed the relevancy of energy performance to CO_(2)emission production and electricity expenses.

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.

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.

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.

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%.

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.

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.

Kinetic analysis and modeling of maize straw hydrochar combustion using a multi-Gaussian-distributed activation energy model

Combustion kinetics of the hydrochar was investigated using a multi-Gaussian-distributed activation energy model(DAEM)to ex-pand the knowledge on the combustion mechanisms.The results demonstrated that the kinetic parameters calculated by the multi-Gaussian-DAEM accurately represented the experimental conversion rate curves.Overall,the feedstock combustion could be divided into four stages:the decomposition of hemicellulose,cellulose,lignin,and char combustion.The hydrochar combustion could in turn be divided into three stages:the combustion of cellulose,lignin,and char.The mean activation energy ranges obtained for the cellulose,lignin,and char were 273.7-292.8,315.1-334.5,and 354.4-370 kJ/mol,respectively,with the standard deviations of 2.1-23.1,9.5-27.4,and 12.1-22.9 kJ/mol,re-spectively.The cellulose and lignin contents first increased and then decreased with increasing hydrothermal carbonization(HTC)temperature,while the mass fraction of char gradually increased.

Determination of energy coupling to material in laser welding by a novel “sandwich” method

A mathematical energy coupling model was developed to analyze the light transmission in the keyhole and energy distribution on the keyhole wall.The main characteristics of the model include：1） a prototype of the keyhole and the inverse Bremsstrahlung absorption coefficient in the keyhole plasma are obtained from the experiments;2） instead of using a parallel incident beam,a focused laser beam with real Gaussian intensity distribution is implemented;3） both Fresnel absorption and inverse Bremsstrahlung absorption during multiple reflections are considered.The calculation results show that the distribution of absorbed laser intensity by the keyhole wall is not uniform.The maximum laser energy is absorbed by the bottom of the keyhole,although no rays irradiate directly onto the bottom.According to analysis of beam focusing characteristics,the location of the focal plane plays a more important role in the laser energy absorption by the front wall than by the rear wall.

Energy Estimation and Optimization Platform for 4G and the Future Base Station System Early-Stage Design

There are already several power models to estimate the power consumption of base stations at system level. However, there is so far no model that can predict power consumption of the future base station designs based on algorithms and hardware selections with insufficient physical information. We present such an energy model for typical base stations. This model can help designers in estimating, evaluating and optimizing energy/power consumption of candidate designs in early design stages. The proposed model is verified by an LTE extreme scenario. The estimated results show that digital front-end, channel equalization and channel decoding are three major power greedy modules(consuming 39.4%, 16.3%, 13.4%) in a digital baseband subsystem. The power estimation error of the proposed power amplifier(PA) power model is 3.5%(macro cell). The major contribution of this paper is that the proposed models can rapidly estimate energy/power consumption of 4G and the future base stations(such as 5G) in early design stages with well acceptable precision, even without sufficient implementation information.

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.

Solar-Assisted Space Heating and Small Pond-Assisted Space Cooling of a Highly Insulated Energy Efficient House

For three weeks in October 2009, the U.S. Department of Energy hosted the Solar Decathlon Competition in which 20 teams of college and university students competed to design, build, and operate their own version of a solar-powered house. Team North＇s mission was to deliver North House, a compelling, marketable solar powered home for people with active lifestyles, while building Canada＇s next generation of leaders in sustainable engineering, business and design. This paper deals with a solar-assisted space heating system that was studied as a potential design for the competition. Among several other conclusions, it was found that using a solar-assisted in-floor heating system can decrease the energy consumption to only 8% of the case without the in-floor loop.

ENERGY PERFORMANCE OF CAMPUS LEED® BUILDINGS: IMPLICATIONS FOR GREEN BUILDING AND ENERGY POLICY

Many university campuses in the United States are working toward their sustain-able goals by adopting energy or green building policies,which require Leadership in Energy and Environmental Design(LEED®)certification for new construction and major renovation projects.Because LEED certification heavily relies on whole building energy simulation to demonstrate building energy performance improve-ment,it is often assumed that the finished buildings will achieve the predicted level of energy efficiency.This paper presents a study that compares the energy model predictions with actual energy performance of three LEED buildings on a univer-sity campus.The study shows that one of the campus LEED buildings consumed twice the predicted energy usage while causing a high level of occupant dissatisfac-tion.Further investigation reveals a variety of contributing factors for these issues and provides insights to improve green building policy and practice.Not only are the research findings important for this particular campus(Ohio State University)on its way to sustainability,they also have widespread ramifications for other uni-versity campuses.

IMPACT OF RETROFITTING ENERGY-EFFICIENT DESIGN STRATEGIES ON ENERGY USE OF EXISTING COMMERCIAL BUILDINGS:COMPARATIVE STUDY OF LOW-IMPACT AND DEEP RETROFIT STRATEGIES

This article discusses cnergy-ficicnt rtfitting design strategics for commercial office buildings,and cxamincs thcir ffect on energy consumption.The objective of the rescarch was to study how to intcgrate passive design stratcgics and cnergy-efficient building systems to improve building performancc,and reduce the cncrgy consumption of existing buildings in three different climate types(cold,mixed and hot climates).First,propertics of existing buildings were analyzed based on national CBECS database to detcrminc typical charactcristics of office buildings located in Chicago,Baltimore and Phoenix,including size,building envelope treatment and building systems.Then,fourteen diffrent prototypes were developed,varying the building shape and orientation to represent different building stock,and cncrgy modcling was conducted to cstablish energy usage baselinc.Multiplc design con-sidcrations were investigated bascd on cxtensivc cncrgy simulations and modeling;where low-impact and decp retrofits were considered.Low-impact stratcgics included improvements to the building cnvelope,lighting systems and optimization of HVAC systems opcration(without upgrading heating and cooling cquipment).Decp cnergy rctrofits also included improvements to building envclopc and lighting,and con-sidered changcs and improvements to HVAC systems(spccifically,integration of radiant systcms).Energy modeling was conducted for all prototypes,and results were obtained for the bascline(current energy usagc),and energy usage considering low-impact design stratcgics and decp rctrofts.A total of 126 cnergy modcls was devcloped,simulated and analyzed,providing a dataset that captured cnergy usage for investigated scenarios.The comparative analysis of simulation results was used to determine how specific techniqucs lead to energy savings in different climatc types,as well as for buildings of various shapes and oricntations.

IDENTIFYING OPTIMUM GLAZING PROPERTY FOR CONSERVING ENERGY IN HOT SEMI-ARID CLIMATE REGIONS

Globally,the building sector is responsible for 40%of energy use and 30%of GHG emissions.The greatest portion of the energy is used during the operational phase(use stage)of buildings.The building envelope,especially the glazed components,plays an important role in determining the energy requirement of buildings.These glazed parts of the building envelope exposed to direct solar radiation are most vulnerable to heat loss and gain.Heat loss and gain through the glazing material depend on glazing properties(U-value,SHGC,VT)and building energy use changes according to the properties of the glazing system.A variety of glazing types has been developed over recent decades that use the properties of the glass as a means of responding to environmental conditions.This study is carried out to identify the optimum glazing property for conserving energy in cooling dominant regions using an early design energy modeling tool.It was found that a low SHGC is the most important glazing property for reducing cooling energy consumption.SHGC of less than 0.3 is found useful.This study would help building industry professionals evaluate the best glazing property while selecting the glazing type.

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.

Accurate energy model for WSN node and its optimal design

With the development of CMOS and MEMS technologies, the implementation of a large number of wireless distributed micro-sensors that can be easily and rapidly deployed to form highly redundant, self-configuring, and ad hoc sensor networks. To facilitate ease of deployment, these sensors operate on battery for extended periods of time. A particular challenge in maintaining extended battery lifetime lies in achieving communications with low power. For better understanding of the design tradeoffs of wireless sensor network （WSN）, a more accurate energy model for wireless sensor node is proposed, and an optimal design method of energy efficient wireless sensor node is described as well. Different from power models ever shown which assume the power cost of each component in WSN node is constant, the new one takes into account the energy dissipation of circuits in practical physical layer. It shows that there are some parameters, such as data rate, carrier frequency, bandwidth, Tsw, etc, which have a significant effect on the WSN node energy consumption per useful bit （EPUB）. For a given quality specification, how energy consumption can be reduced by adjusting one or more of these parameters is shown.

Prospects of key technologies of integrated energy systems for rural electrification in China

Owing to increasing environmental concerns and resource scarcity, integrated energy system shave become widely used in communities. Rural energy systems, as one of the important links of the energy network in China, suffer from low energy efficiency and weak infrastructure. Therefore, it is particularly important to increase the proportion of electricity consumption and build an integrated energy system for rural electrification in China(IESREIC) with a rural distribution network as the core, in line with national conditions. In this study, by analyzing the Chinese regional differences and natural resource endowments, the development characteristics of the IESREIC are summarized. Then, according to the existing rural energy problems, key technologies are proposed for the IESREIC, such as those for planning and operation, value sharing, infrastructure, and a management and control platform. Finally, IESREIC demonstration projects and business models are introduced for agricultural production, rural industrial systems, and rural life. The purpose is to propose research concepts for the IESREIC, provide suggestions for the development of rural energy, and provide a reference for the construction of rural energy systems in countries with characteristics similar to those of China.