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.

Discussion on the Intelligent Design of Ultra-Low Energy Consumption Passive Buildings

With the continuous development of science and technology and the gradual improvement of modem building technology, people pay more and more attention to the introduction of advanced technology in architectural design, such as the application of intelligent technology. With the increasingly severe environmental situation, people are increasingly demanding the environmental performance and green performance of buildings. The establishment of ultra-low energy consumption passive buildings has become one of the key construction contents of construction projects. This paper mainly analyzes the design points and architectural forms of related buildings from the perspective of intelligent control.

Analyses of influence of residential buildings' space organization on heating energy consumption in Lhasa

The heating load simulation models of the residential buildings in Lhasa are established for enhancing the space organization’s adaptability to climate and radiation and improving its energy saving performance.The space organization items a e analyzed for both the existing buildings without insulation and new buildings with good insulation.The items include orientation design,south a d north balcony design,the north and south partition wall’s position design,storey height design and window-wall ratio design.Simulation results show that orientation is the key design element for energy saving design,and adverse orientation can obviouslyincrease heating energy consumption;south and north balconies can reduce winter heating energy consumption;partition walls move to the north,which means that the south room’s big depth design leads to less heating energy consumption,but the effect is not inconspicuous;smaier storey height results in less heating load.For the existing buildings,the window-wall ratio of south side has a balance point for energy saving design in the calculation condition.For the new buildings with good insulation,enlarging the south window-wal ratio can continuously reduce heating energy consumption,but the energy saving rate between models gets smaier.The heating energy consumption comparison study between the common model and optimal space design model demonstrates that the energy saving design can significantly reduce heating energy consumption

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.

Modeling and optimization of a multi-carrier renewable energy system for zero-energy consumption buildings

For the carbon-neutral,a multi-carrier renewable energy system(MRES),driven by the wind,solar and geothermal,was considered as an effective solution to mitigate CO2emissions and reduce energy usage in the building sector.A proper sizing method was essential for achieving the desired 100%renewable energy system of resources.This paper presented a bi-objective optimization formulation for sizing the MRES using a constrained genetic algorithm(GA)coupled with the loss of power supply probability(LPSP)method to achieve the minimal cost of the system and the reliability of the system to the load real time requirement.An optimization App has been developed in MATLAB environment to offer a user-friendly interface and output the optimized design parameters when given the load demand.A case study of a swimming pool building was used to demonstrate the process of the proposed design method.Compared to the conventional distributed energy system,the MRES is feasible with a lower annual total cost(ATC).Additionally,the ATC decreases as the power supply reliability of the renewable system decreases.There is a decrease of 24%of the annual total cost when the power supply probability is equal to 8%compared to the baseline case with 0%power supply probability.

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.

Energy consumption prediction model of typical buildings in hot summer and cold winter zone of China

To overcome the shortcomings of the energyconsumption prediction models in the application during thedesign stage, a quick prediction model for energy consumptionis proposed based on the decoupling method. Taking typicalresidential and office buildings in hot summer and cold winterzones as research objects, the influence factors on buildingenergy consumption are classified into intrinsic factors andoperational factors on the basis of the heat transfer principle.Then, using the intrinsic factors as the fundamental variablesand operational factors as the modified variables, the quickprediction model for the buildings in typical cold and hot zonesis proposed based on the decoupling method and the accuracyof the proposed model is verified. The results show thatcompared to the simulation results of EnergyPlus, the relativeerror of the prediction model is less than 1.5% ; comparedwith the real operating data of the building, the relative erroris 13.14% in 2011 and 8.56% in 2012 due to the fact that thecoincidence factor becomes larger than the design value about16% in 2011 and 13% in 2012. The finding reveals that theproposed model has the advantages of rapid calculationcompared with EnergyPlus and Design Builder when predictingbuilding energy consumption in building designs. The energyconsumption prediction model is of great practical value inoptimal operation and building designs.

Energy consumption,indoor environmental quality,and benchmark for office buildings in Hainan Province of China

With rapid economy growth,building energy consumption in China has been gradually increased.The energy consumption and indoor environmental quality of 51 office buildings in Hainan Province,a hot and humid area,were studied through collection of verified data in site visits and field tests.The result revealed that,electricity accounted for 99.79% of the total energy consumption,natural gas 0.17%,and diesel 0.04%.The air conditioning dominated the energy use with a share of 43.18%,equipment in the particular areas 26.90%,equipment in the office rooms 11.95%,lighting system 8.67%,general service system 7.57%,and miscellaneous items 1.73%.Statistical method including six indicators obtained the energy consumption benchmark with upper limit of 98.31 kW-h/m2 and lower limit of 55.26 kW-h/m2.According to ASHRAE standard(comfortable standard) and GB/T 18883-2002(acceptable standard),the indoor environmental quality of 51 sampled office buildings was classified into three ranks:good,normal and bad.With benchmark of building energy consumption combined with indoor environmental quality,it was found that only 3.92% of sampled buildings can be identified as the best performance buildings with low energy consumption and advanced indoor environmental quality,and the buildings classified into normal level accounted for the maximum ratio.

Energy Consumption Monitoring Analysis for Residential, Educational and Public Buildings

In the present article thermal and electrical energy consumptions for different types of buildings are analyzed. The latitude and longitude of the researched area are defined 59?00'N and 26?00'E. According to K?ppen climate classification the area is located in warm summer continental climate. The study consist 40 residential, 7 educational and 44 public buildings. Three years data for each building type among 2006-2011 was used. Several detailed energy balances are presented for apartment buildings. In addition the different ways of domestic hot water preparation are analyzed for apartment buildings. The school buildings average consumption values are represented in study. Also valuable information of measured electrical energy consumption balance for a new office building is presented. Finally there is included the energy consumption analysis of public buildings.

The Impacts of Building Regulations on the Thermal Performance and Energy Consumption of Residential Buildings in Riyadh City-Saudi Arabia

Riyadh city is the fastest growing city in Saudi Arabia. The rapid urban growth that happened recently in Riyadh was not based on the traditional urban planning principles, which have been established and applied for the city development process. The imported building regulations have created a new urban structures and street patterns. The contemporary urban form in Riyadh city is based mainly on traffic and economic consideration with the neglect of environmental dimensions. This research aims to examine the impacts of building regulations on the thermal performance of residential buildings in Riyadh city, with the ultimate goal of establishing planning guidelines that consider the environmental conditions of the city. The methodology adopted for achieving the aim of this study consists of two phases. First, the literature related to building regulations development in Riyadh, as of 2018, was reviewed. Second, buildings energy simulation was conducted to examine the thermal performance of the typical current status of residential building blocks in Riyadh city, and then several changes to building regulations were made to investigate their impacts on the thermal performance of buildings. The results showed that the impacts of Riyadh building regulations on the thermal performance of residential buildings differ across the evaluated cases. The ratio of building height to street width, urban block street orientation, and building orientation are the main factors affecting thermal performance of buildings within urban block. The study also concludes that adjusting the ratio of building height to the distance between buildings could have a significant impact in reducing cooling loads. This study will help policy makers, planners and designers to investigate the shortcoming in the current building regulations.

Characteristics of Changes in Energy Consumption of Rural Residential Buildings from the Perspective of Province and Recommendations

In the context of the new period,the living standards and comfort demands of rural residents are increasing,which promotes the continuous growth of the total energy consumption of rural residential buildings.In this study,it estimated the total energy consumption of rural residential buildings in 30 provinces(or cities)in China from 2004 to 2016.Through the division of climate regions of the residential buildings,this paper analyzed the characteristics of changes in the energy structure of residential buildings and the trend of energy consumption from the perspective of the province.Then based on the people s livelihood and from the perspective of development,it came up with some pertinent strategies and recommendations for energy saving and emission reduction in rural residential buildings.

The Effects of Future Climate Change on Energy Consumption in Residential Buildings in China

China is currently going through a phase of rapid mass urbanisation, and it is important to investigate how the growing built environment will cope with climate change, to see how the energy consumption of buildings in China will be affected. This is especially important for the fast-growing cities in the north, and around the east and south coasts. This paper aims to study the effects of future climate change on the energy consumption of buildings in the three main climate regions of China, namely the “Cold” region in the north, which includes Beijing;the “Hot Summer Cold Winter” region in the east, which includes cities such as Shanghai and Ningbo;and the “Hot Summer Mild Winter” region in the south, which includes Guangzhou. Using data from the climate model, HadCM3, Test Reference Years are generated for the 2020s, 2050s and 2080s, for various IPCC future scenarios. These are then used to access the energy performance of typical existing buildings, and also the effects of retrofitting them to the standard of the current building codes. It was found that although there are reductions in energy consumption for heating and cooling with retrofitting existing residential buildings to the current standard, the actual effects are very small compared with the extra energy consumption that comes as a result of future climate change. This is especially true for Guangzhou, which currently have very little heating load, so there is little benefit of the reduction in heating demand from climate change. The effects of retrofitting in Beijing are also limited, and only in Ningbo was the effect of retrofitting able to nullify the effects of climate change up to 2020s. More improvements in building standards in all three regions are required to significantly reduce the effects of future climate change, especially to beyond 2020s.

Smart Building Design to Improve the Energy Consumption at an Office Room

Buildings are becoming smarter as a result of a variety of advanced technologies that enable energy management, optimal space utilization, and smart surveillance for safety, among other things. Energy-efficient smart building ideas and execution are of great interest and top priority due to the building’s occupants’ misused and high-power consumption. This paper addresses the design and execution of an energy management system that includes a solar power system for generating power for the building’s needs and a PIR-based automation system for efficient power use. This project was carried out at the Military Technological College (MTC) in Muscat, in the system engineering department’s offices. This project seeks to generate power for the building’s energy needs using solar photovoltaic panels and reduce energy consumption within the office using a PIR-based automation system. The results demonstrate that after the breakeven point (the time it takes to recoup the initial investment), it can provide power to the building for another 17 years. The calculations and practical results presented in this study approve that the system is extremely helpful.

A Data Analytics Study on Building Characteristics Impacting Energy Consumption in Single-Family Attached Homes

San Antonio, Texas is the seventh largest city in the United States with a population of 1.4 million people, and ranked among the fastest growing cities. To assess the implications of past and present building practices within the residential sector on future energy consumption, the energy utilization of single-family attached homes (SFAH) in Bexar County, Texas is studied. The available dataset includes 3932 SFAH records representing about 33% of the total number of SFAHs within the county. The study is based on pairing and analyzing data at the individual building level from a variety of sources including the buildings’ physical characteristics, access to fuels, and monthly energy consumption. The results indicate that the area of conditioned space, presence of swimming pools, number of stories, presence of fireplaces, fuel-type, and number of shared walls are a significant factor on the energy consumption of single-family attached homes. In terms of energy consumption, all-electric two-story homes sharing two walls are the most energy efficient among SFAHs. This study can aid comprehensive master planning efforts for developing sustainable communities by highlighting key features of SFAHs and making the case for higher density housing as a viable and more energy efficient alternative to single-family detached homes (SFDH).

Comparison of Energy Consumption of the 12 Classroom Typical School Buildings in Selected 4 City in Turkey

School is a special place where students come together to become productive individuals of society,acquire basic skills and acquire citizenship knowledge.With the introduction of the new education system(4+4+4)in Turkey in 2012-2013,some difficulties occurred in the spatial structure of the schools.After the new system,increasing number of students and decreasing student requirements have been tried to be solved with temporary solutions.At the same time that millions of students studying in primary schools all over Turkey have the same architectural feature as one type of architectural school project,regardless of the geographical and social situation began to be implemented in all parts of the city.Therefore,the increase in consumption varies depending on the geographical reasons where the type projects are implemented.Selected regions of the four thermal zones in Turkey for this research are provided below:1^st Thermal district in Antalya;2^nd Thermal district in Bursa;3^rd Thermal district in Elaz??;4^th Thermal district in Kars.The calculation of the energy consumption created by the above cities by means of BEP-TR program and comparing classes.

Evaluation of Energy Consumption for Heating of Industrial Building in-Situ

Because of the high energy demand required to heat a production hall, the aim of this project is to find out whether it is possible to verify the heating consuming process for heating with the standard simplified calculation method [1], especially for cold regions such as Kosice (Slovakia). The energy requirement for heating a case study industrial building was evaluated using measurements and calculations.During the winter period, energy consumption was measured in the selected industrial building according to a validation standard [2]. The building is comprised of two halls. The measurements were analyzed according to the criteria used for validating residential and public buildings, with several regression dependencies taken into account in the resulting evaluation of heating energy consumption. The mathematical dependencies of measured values in real conditions are shown in this paper. In addition, the building’s heating energy demand was calculated according to the Austrian standard [3], ?NORM EN ISO 13790, the simplified calculation method for non-residential buildings. It was investigated whether the measured values could be replicated using this calculation. It was found that the precise definition of the internal heat gains is very important.

A hybrid agent⁃based machine learning method for human⁃centred energy consumption prediction

Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.

Energy Consumption Analysis and Characterization of Aerospace Manufacturing Facilities in the United States–A Step towards Sustainable Development

In this study,information on energy usage in the United States(U.S.)aerospace manufacturing sector has been analyzed and then represented as energy intensities(kWh/m2)to establish benchmark data and to compare facilities of varying sizes.First,public sources were identified and the data from these previously published sources were aggregated to determine the energy usage of aerospace manufacturing facilities within the U.S.From this dataset,a sample of 28 buildings were selected and the energy intensity for each building was estimated from the data.Next,as a part of this study the energy data for three additional aerospace manufacturing facilities in the U.S.were collected firsthand.That data was analyzed and the energy intensity(kWh/m2)for each facility was calculated and then compared with the energy intensities of the 28 buildings from the sample.Three different indicators of energy consumption in aerospace manufacturing facilities were used as comparators to assist facility managers with determining potential energy savings and help in the decision-making process.On average,aerospace manufacturing facilities in the United States spent 4 cents for each dollar of sale on energy.The energy intensity(kWh/m2)and the power intensity(W/m2)for each facility were calculated based on the actual facility energy bills.The power intensity for these facilities ranges from 34 to 134 W/m2.The energy intensity ranged from 232 to 949 kWh/m2.We found that the power intensity could be used to estimate energy consumption when the annual operating hours of the facility are considered.and to estimate the energy-related carbon dioxide emissions.

Efficiency-based Pareto Optimization of Building Energy Consumption and Thermal Comfort:A Case Study of a Residential Building in Bushehr,Iran

In Iran,the intensity of energy consumption in the building sector is almost 3 times the world average,and due to the consumption of fossil fuels as the main source of energy in this sector,as well as the lack of optimal design of buildings,it has led to excessive release of toxic gases into the environment.This research develops an efficient approach for the simulation-oriented Pareto optimization(SOPO)of building energy efficiency to assist engineers in optimal building design in early design phases.To this end,EnergyPlus,as one of the most powerful and well-known whole-building simulation programs,is combined with the Multi-objective Ant Colony Optimization(MOACO)algorithm through the JAVA programming language.As a result,the capabilities of JAVA programming are added to EnergyPlus without the use of other plugins and third parties.To evaluate the effectiveness of the developed method,it was performed on a residential building located in the hot and semi-arid region of Iran.To obtain the optimum configuration of the building under investigation,the building rotation,window-to-wall ratio,tilt angle of shading device,depth of shading device,color of the external walls,area of solar collector,tilt angle of solar collector,rotation of solar collector,cooling and heating setpoints of heating,ventilation,and air conditioning(HVAC)system are chosen as decision variables.Further,the building energy consumption(BEC),solar collector efficiency(SCE),and predicted percentage of dissatisfied(PPD)index as a measure of the occupants'thermal comfort level are chosen as the objective functions.The single-objective optimization(SO)and Pareto optimization(PO)are performed.The obtained results are compared to the initial values of the basic model.The optimization results depict that the PO provides optimal solutions more reliable than those obtained by the SOs,owing to the lower value of the deviation index.Moreover,the optimal solutions extracted through the PO are depicted in the form of Pareto fronts.Eventually,the Linear Programming Technique for Multidimensional Analysis of Preference(LINMAP)technique as one of the well-known multi-criteria decision-making(MCDM)methods is utilized to adopt the optimum building configuration from the set of Pareto optimal solutions.Further,the results of PO show that although BEC increases from 136 GJ to 140 GJ,PPD significantly decreases from 26%to 8%and SCE significantly increases from 16%to 25%.The introduced SOPO method suggests an effective and practical approach to obtain optimal solutions during the building design phase and provides an opportunity for building engineers to have a better picture of the range of options for decision-making.In addition,the method presented in this study can be applied to different types of buildings in different climates.