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.

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.

Influence of urban morphological factors on building energy consumption combined with photovoltaic potential: A case study of residential blocks in central China

Studies on urban energy have been growing in interest,and past research has mostly been focused on studies of urban solar potential or urban building energy consumption independently.However,holistic research on the combination of urban building energy consumption and solar potential at the urban block-scale is required in order to minimize energy use and maximize solar power generation simultaneously.The aim of this study is to comprehensively evaluate the impact of urban morphological factors on photovoltaic(PV)potential and building energy consumption.Firstly,58 residential blocks were classified into 6 categories by k-means clustering.Secondly,3 energy performance factors,which include the energy use intensity(EUI),the energy use intensity combined with PV potential(EUI-PV),and photovoltaic substitution rate(PSR)were calculated for these blocks.The study found that the EUI of the Small Length&High Height blocks was the lowest at around 30 kWh/(m^(2)·y),while the EUI-PV of the Small Length&Low Height blocks was the lowest at around 4.45 kWh/(m^(2)·y),and their PSR was the highest at 87%.Regression modelling was carried out,and the study concluded that the EUI of residential blocks was mainly affected by shape factor,building density and floor area ratio,while EUI-PV and PSR were mainly affected by height and sky view factor.In this study,the results and developed methodology are helpful to provide recommendations and strategies for sustainable planning of residential blocks in central China.

Federated learning-based short-term building energy consumption prediction method for solving the data silos problem

Transfer learning is an effective method to predict the energy consumption of information-poor buildings by learning transferable knowledge from operational data of information-rich buildings.However,it is not recommended to directly use the operational data without protection due to the risk of leaking occupants’privacy.To address this problem,this study proposes a federated learning-based method to learn transferable knowledge from building operational data without privacy leaking.It trains a transferable federated model based on the operational data from the buildings similar to the target building with limited data.An advanced secure aggregation algorithm is adopted in the training process to ensure that no one can infer private information from the training data.The federated model obtained is evaluated by comparing it with the standalone model without federated learning based on 13 similar office buildings from the Building Data Genome Project.The results show that the federated model outperforms the standalone model concerning the prediction accuracy and training time.On average,the federated model achieves a 25.4%decrease in CV-RMSE when the target building has limited operational data.Even if the target building has no operational data,the federated model still achieves acceptable accuracy(CV-RMSE is 22.2%).Meanwhile,the training time of the federated model is 90%less than that of the standalone model.The research insights can help develop federated learning-based methods for solving the data silos problem in building energy management.The methodology and analysis procedures are reproducible and all codes and data sets are available on Github.

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,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.

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.

Energy consumption simulations of rural residential buildings considering differences in energy use behavior among family members

The“average occupant”methodology is widely used in energy consumption simulations of residential buildings;however,it fails to consider the differences in energy use behavior among family members.Based on a field survey on the Central Shaanxi Plain,to identify the energy use behavior patterns of typical families,a stochastic energy use behavior model considering differences in energy use behavior among family members was proposed,to improve the accuracy of energy consumption simulations of residential buildings.The results indicated that the surveyed rural families could be classified into the following four types depending on specific energy use behavior patterns:families of one elderly couple,families of one middle-aged couple,families of one elderly couple and one child,and families of one couple and one child.Moreover,on typical summer days,the results of daily building energy consumption simulation obtained by the“average occupant”methodology were 25.39%and 28%lower than the simulation results obtained by the model proposed in this study for families of one elderly couple and families of one middle-aged couple,and 13.05%and 23.05%higher for families of one elderly couple and one child,and families of one couple and one child.On typical winter days,for the four types of families,the results of daily building energy consumption simulation obtained by the“average occupant”methodology were 21.69%,10.84%,1.21%,and 8.39%lower than the simulation results obtained by the model proposed in this study,respectively.

The Application of Solid Waste in Thermal Insulation Materials: A Review

As socioeconomic development continues,the issue of building energy consumption has attracted significant attention,and improving the thermal insulation performance of buildings has become a crucial strategic measure.Simultaneously,the application of solid waste in insulation materials has also become a hot topic.This paper reviews the sources and classifications of solid waste,focusing on research progress in its application as insulation materials in the domains of daily life,agriculture,and industry.The research shows that incorporating household solid waste materials,such as waste glass,paper,and clothing scraps into cementitious thermal insulation can significantly reduce the thermal conductivity of the materials,leading to excellent thermal insulation properties.Insulation materials prepared from agricultural solid waste,such as barley straw,corn stalk,chicken feather,and date palm fibers,possess characteristics of lightweight and strong thermal insulation.Industrial solid waste,including waste tires,iron tailings,and coal bottom ash,can also be utilized in the preparation of insulation materials.These innovative applications not only have positive environmental significance by reducing waste emissions and resource consumption,but also provide efficient and sustainable insulation solutions for the construction industry.However,to further optimize the mix design and enhance the durability of insulation materials,continuous research is required to investigate the mechanisms through which solid waste impacts the performance of insulation materials.

Research on the Design and Energy Saving of New Rural Residential in the Southwest Area

The paper use advantage of local natural resources, greening and sufficient water resources, combine natural ecological environment design with rural architecture, and fully consider local economic base and material technical conditions, and takes site selection and planning, architectural design technology as the two core aspects to study rural residential energy conservation, improving thermal environment of indoor residential that use of nature building energy saving technique, efforts to reduce the use of mechanical equipment system, thereby reducing the rural residential building energy consumption.

An artificial intelligence framework for explainable drift detection in energy forecasting

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building Science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

Contribution of nearly-zero energy buildings standards enforcement to achieve carbon neutral in urban area by 2060

Nearly-zero energy buildings (NZEB) would effectively improve building energy efficiency and promote building electrification. By using a carbon emission model integrated into a bottom-up mid-to-long term energy consumption model, this study analyzes the contribution of NZEB standards to carbon emission targets in the urban area of China by 2060. Three scenarios are set, namely BAU, steady development (S1), and high-speed development (S2). For BAU, the total carbon emissions will reach a peak of 1.94 Gt CO_(2) by 2040. In S1 scenario, total building carbon emissions will reach the peak of 1.72 Gt CO_(2) by 2030. In S2 scenario, the carbon emissions will reach a peak by 2025 with 1.64 Gt CO_(2). Under S1 scenario, which features consistency with NZEB market development and periodic improvement of building energy-efficiency standards, the carbon emission peak in 2030 will be accomplished. To achieve carbon neutrality by 2060, the upgrading of building energy standards to NZEB will contribute 50.1%, while zero-carbon electricity contribution is 49.9%. It is concluded that 2025, 2030, and 2035 could be set as mandatory enforcement years for ultra-low energy buildings, NZEB and zero energy building (ZEB), respectively.

The challenges on operating a zero net energy building facing global warming conditions

This study proposes a methodology to evaluate the energy performance of existing Zero Energy Buildings and to prospect retrofit strategies in a Savannah climate,concerning the A2 scenario of emissions from the Fourth Report of the Intergovernmental Panel on Climate Change.The selected building to study is recognized for its high energy performance,named Centro SEBRAE de Sustentabilidade(CSS).Two efficient measures were considered:(i)improvement in the air conditioning system coefficient of performance(COP)and(ii)in the energy efficiency of the photovoltaic plates of generation on-site.The methodology is grounded in the potential bioclimatic concept and the employed steps applied were:preparation of climate archives in the 2020,2050 and 2080 time-slices;calibration of the computational model;evaluation of the retrofit strategies on its energy consumption efficiency through computer simulation.Considering the CSS has already attended mostly the bioclimatic strategies for the local climate and has high efficiency measures in its systems,the retrofit focused the air conditioning and PV system.The isolated retrofit of the air conditioning system was unable to guarantees the NZEB condition despite providing an adequate level of energy efficiency until 2080.The retrofit of the PV system was the only one that provides the NZEB condition for climate change scenarios.The contribution of this paper is to provide a guide to be used in NZEBs,with measures of optimization that provide high potential bioclimatic face to the local where it is built,making it possible to maintain this condition in scenarios of global warming.

Assessing renewable-energy potential and feasibility in Tehran’s buildings

This article examines the results of using renewable energy to reduce the energy consumption of buildings significantly.In particular,it looks at the results in a country such as Iran,which has a high potential for using solar energy.A comparison of the energy consumption of selected case samples based on the type of ownership of private,government and municipal buildings in 22 districts of Tehran has been analysed.Using data for energy consumption and Strengths,Weaknesses,Opportunities,Threats analysis includes open and usable spaces for installing renewable-energy systems in 10%of public buildings,4%of private facilities and 10%of municipal buildings.The results of this study show that the average energy consumption of buildings in Tehran is almost four times the global average.Iran has~300 days of sunlight for installing solar panels in any place where solar energy is in direct contact with the Sun.Thus,it allows the building to use the energy absorbed by the discussions in all seasons.In 2050,this country could play a decisive role in producing renewable energy.In addition,solar energy may reduce fossil-fuel consumption and production costs.

Building climate zones of major marine islands in China defined using two-stage zoning method and clustering analysis

The current scheme of building climate zones in China generally assumes that building climate zones of island cities are identical to adjacent land stations.Consequently,building design strategies for island buildings usually refer to those developed for inland cities.This approach has to some extent hindered the energy-saving design and green development of island buildings in China.This research takes a first step on this issue by defining the building climate zones of 36 marine islands over China marine area using two-stage zoning methodology adopted by current building climate zoning standard(GB50178-1993).The meteorological data used for analysis was obtained from the National Climate Center of China over the 30-year period from 1985 to 2014.As comparison,40 coastal stations which are adjacent to the inves-tigated marine islands were also included in this study.Subsequently a more obiective techni-que-cluster analysis was operated as an effective supplement to discover the climate characteristics among different observations.The results of both methodologies consistentlyshow that among the 36 islands investigated,the majority of islands located in northern and eastern marine area belong to the same climate zones as their adjacent coastal cities.Howev-er,island cities in southern marine area cannot be assigned to any current climate zone,which was demonstrated by its distinctive climate features different from any other sites investi-gated through cluster analysis as well as different energy use patterns.Thus a new zone was defined to supplement the current building climate zoning scheme to cover marine area of China.

Performance of aerogel as a thermal insulation material towards a sustainable design of residential buildings for tropical climates in Nigeria

The building sector accounts for nearly 40%of global energy consumption.In Nigeria,more than two-thirds of the consumption comes from residential buildings.Energy efficiency measures through the adoption of insulation materials are tools that could crash the peak demand of energy in buildings while improving its thermal comfort and aerogel is considered as the most effective material for insulation,owing to its unique thermal properties.In this paper,we present the performance of aerogel as a thermal insulation material towards a sustainable design of residential buildings for tropical climates in Nigeria.First,a typical residential building in the tropical region was modeled with conventional materials utilized in the region and was later modified through the application of aerogel material on various surfaces of the model.A whole building energy simulation was then carried out in each variation and the outcome was compared to effectively conclude on the significance of aerogel in terms of thermal comfort improvement and energy consumption reduction.Results show that aerogel had the highest influence when inserted in the attic and floor slabs of the designed model.A reduction of more than 6%was attained in the recorded indoor mean air and operative temperatures while still maintaining an acceptable humidity range.Concerning energy consumption,a reduction of more than 15%was achieved.However,the high price of aerogel may hinder its application on the studied building but could be a good investment where climate change and sustainability are of high importance and less concern is given to expenditure.Aerogel demonstrated significant potential with respect to both thermal comfort improvement and energy consumption reduction on the designed model.The outcome of the study is hoped to serve as a base reference for the insulation of residential buildings with similar climate and characteristics to the adopted case building.

A review and prospect on research progress of adjustable transparent envelope

Considered as the best light source so far,daylight has attracted continuing attention in indoor environment design area.Successful daylighting design could reduce considerable amount of building energy consumption,while retain a satisfactory occupant comfort and working efficiency.Transparent building envelope takes a dominant position in daylighting design as well as solar radiation heat gain,thus attracts attentions from all over the world.Unable to respond to dynamic outdoor environmental parameters,conventional transparent envelope cannot adapt to the continuous development of green building performance requirements,thus the adjustable transparent envelope technologies have become the research focus.In this paper,recent progress on adjustable transparent envelope technologies was collected and analyzed.Their detailed working principle as well as application scope were classified and discussed.Result indicates that existing studies mainly focus on the development of material and equipment.In the aspect of comprehensive application optimization design and control strategy development,the research gap still exists.

Courtyard design impact on indoor thermal comfort and utility costs for residential households: Comparative analysis and deep-learning predictive model

A courtyard is an architectural design element which is often known as microclimate modifiers and is responsible to increase the indoor occupant comfort in traditional architecture. The aim of this study is to conduct a parametric evaluation of courtyard design variants in a residential building of different climates with a focus on indoor thermal comfort and utility costs. A brute-force approach is applied to generate a wide range of design alternatives and the simulation workflow is conducted by Grasshopper together with the environmental plugins Ladybug and Honeybee. The main study objective is the evaluation of the occupant thermal comfort in an air-conditioned residential building, energy load, and cost analysis, derived from different design variables including courtyard geometry, window-to-wall ratio, envelope materials, heating, and cooling set-point dead-bands, and building geographical location. Furthermore, a Deep Learning model is developed using the inputs and outputs of the simulation and analysis to transform the outcomes into the algorithmic and tangible environment feasible for predictive applications. The results suggest that regarding the thermal loads, costs, and indoor thermal comfort index (PMV), there are high correlations between the outdoor weather variation and dead-band ranges, while in extreme climates such as Singapore, courtyard spaces might not be efficient enough as expected. Finally, the highly accurate deep learning model is also developed, delivering superior predictive capabilities for the thermal comfort and utility costs of the courtyard designs.