Trombe walls with porous medium insertion and their influence on thermal comfort in flats in Cusco,Peru

The Trombe wall is a passive indirect heating system which should be used in Cusco,Peru to improve thermal conditions against the typical frosts and low temperatures during winter in the high Andean regions.Due to this problem,the use of a modified Trombe wall with insertion of porous medium is proposed to improve thermal comfort in flat buildings in Cusco.This research aims to analyse and compare the performance of dwellings without Trombe wall,with traditional Trombe wall,and with Trombe wall with glass and plastic pellets insertion in thermal comfort improvement.Autodesk■CFD was used to simulate and analyse the system.The simulation was performed with three prototype flats(55 m^(2),75 m^(2) and 95 m^(2))for six months of the year.From the results obtained,the level of thermal comfort in the traditional scenarios is low with an average PMV of-1.86,in the scenarios with Trombe wall is good and in the scenarios with Trombe wall with insertion of porous medium is slightly better than the previous one,reaching an average PMV of+0.10 and a temperature of 21.90℃.The study carried out is important because it represents an efficient eco-sustainable heating alternative that improves the thermal comfort sensation in houses during the coldest months of the year.

Generative pre-trained transformers(GPT)-based automated data mining for building energy management:Advantages,limitations and the future

Advanced data mining methods have shown a promising capacity in building energy management.However,in the past decade,such methods are rarely applied in practice,since they highly rely on users to customize solutions according to the characteristics of target building energy systems.Hence,the major barrier is that the practical applications of such methods remain laborious.It is necessary to enable computers to have the human-like ability to solve data mining tasks.Generative pre-trained transformers(GPT)might be capable of addressing this issue,as some GPT models such as GPT-3.5 and GPT-4 have shown powerful abilities on interaction with humans,code generation,and inference with common sense and domain knowledge.This study explores the potential of the most advanced GPT model(GPT-4)in three data mining scenarios of building energy management,i.e.,energy load prediction,fault diagnosis,and anomaly detection.A performance evaluation framework is proposed to verify the capabilities of GPT-4 on generating energy load prediction codes,diagnosing device faults,and detecting abnormal system operation patterns.It is demonstrated that GPT-4 can automatically solve most of the data mining tasks in this domain,which overcomes the barrier of practical applications of data mining methods in this domain.In the exploration of GPT-4,its advantages and limitations are also discussed comprehensively for revealing future research directions in this domain.

The relationship between indoor air quality(IAQ)and perceived air quality(PAQ)-a review and case analysis of Chinese residential environment

Due to the majority of time people spent indoors,indoor air quality is not only critical to people’s health,comfort,but can also significantly influence occupants perception on indoor environment.Air quality is closely related to many factors including thermal parameters,pollutant concentrations,and ventilation performance.However,the current building indoor air quality evaluation method is based the objective measurement of indoor parameters,without considering occupants’subjective perception.This paper is aimed at summarizing a profound review on the PAQ evaluation method.Comparisons among various PAQ evaluating methods with advantages,similarities and differences were conducted.Analysis of literatures about indoor air quality in Chinese residential buildings field is also summarized,and discussion on the subjective influence of temperature and relative humidity,venti-lation performance,volatile organic compounds(VOCs)concentration,and particulate matters on perceived air quality is carried out.

Thermal comfort in a building with Trombe wall integrated with phase change materials in hot summer and cold winter region without air conditioning

Trombe wall and phase change materials(PCMs)are two effective ways to regulate indoor thermal comfort.However,Trombe wall surfers from overheating in summer and PCMs suffer from low heat transfer rate caused by the limited thermal conductivity.To compensate the shortcomings of the two methods,this paper proposed a Trombe wall system integrated with PCMs.Based on a light-weight building envelope in Changsha,China,the thermal comfort of 10 kinds of Trombe wall systems with PCMs with a melting temperature of 18-28℃ were studied.Taking the integrated indoor discomfort duration(I_(D)),integrated indoor discomfort degree-hour(I_(DH)),indoor air temperature(T_(in)),PCM liquid fraction(γ)and heat flux across wall(q)as evaluation indexes,the indoor thermal comfort was assessed in hot summer and cold winter region.Results show that the Trombe wall helped PCMs complete the phase change process effectively.Trombe wall with PCM25 next to the wall inner surface possessed the lowest annual I_(D) and I_(DH),as 2877 h and 12,974℃·h,respectively.Compared with the values in a traditional building,the I_(D) and I_(DH) were reduced by 7.01% and 14.14%.In order to give full play to the heat storage and heat release of the Trombe wall with PCMs,PCMs with phase change temperature 7℃ lower than the peak ambient temperature in summer or 8℃ higher than the winter night temperature was recommended according to regional climate conditions.

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

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

Predicting moisture condensation risk on the radiant cooling floor of an office using integration of a genetic algorithm-back-propagation neural network with sensitivity analysis

Pre-dehumidification time(τ_(pre))and pre-dehumidification energy consumption(E_(pre))play important roles in preventing the condensation of moisture on the floors of rooms that use a radiant floor cooling(RFC)system.However,there are few theoretical or experimental studies that focus on these two important quantities.In this study,an artificial neural network(ANN)was used to predict condensation risk for the integration of RFC systems with mixed ventilation(MV),stratum ventilation(SV),and displacement ventilation(DV)systems.A genetic algorithm-back-propagation(GA-BP)neural network model was established to predict τ_(pre) and E_(pre).Both training data and validation data were obtained from tests in a computational fluid dynamics(CFD)simulation.The results show that the established GA-BP model can predict τ_(pre) and E_(pre) well.The coefficient of determination(R^(2))of τ_(pre) and of E_(pre) were,respectively,0.973 and 0.956.For an RFC system integrated with an MV,SV,or DV system,the lowest values of τ_(pre) and E_(pre) were with the DV system,23.1 s and 0.237 kWh,respectively,for a 67.5 m^(3) room.Therefore,the best pre-dehumidification effect was with integration of the DV and RFC systems.This study showed that an ANN-based method can be used for predictive control for condensation prevention in RFC systems.It also provides a novel and effective method by which to assess the pre-dehumidification control of radiant floor surfaces.

Climate chamber investigation of the effect of indoor thermal histories on thermal adaptation in different seasons

The indoor thermal history of residents in the hot summer and cold winter climate zone in China have undergone a significant change in recent years,which also changes their seasonal thermal adaptations and this has formed feed-back effects to the increasing usage of air conditioning units in this area.To study the seasonal variations of thermal adaptation,the thermal comfort experiments were conducted on two groups of participants.Each groups included 20 participants who had indoor history mainly with natural ventilation(NV group)and air-conditioning(AC group),respectively.The results demonstrated that the thermal sensation vote(TSV)in warm environments did not differ between AC and NV subjects in summer.However,the TSV of AC subjects were much lower than that of NV subjects in the same standard effective temperature and skin temperature in a cold environment in winter.Overall,the participants who spent most of their time in AC space in winter showed a low level of thermal adaptation with a narrower acceptable skin temperature range of 32.6-33.5℃.Thus,this study presents the basic information regarding the seasonal effects on human thermal adaptation due to different long-term indoor thermal histories.

Experimental study and numerical modeling of the thermal behavior of an industrial prototype ceramic furnace:Energy and environmental optimization

This article focuses on the experimental and numerical study of an industrial prototype furnace intended for the production of ceramics in order to improve the energy efficiency and therefore optimize the fuel consumption and the corresponding carbon dioxide emissions.In order to understand the thermal behavior from which stems the energy efficiency of the experimental prototype,we establish in this work,a simplified modeling allowing to establish a mathematical model describing the thermal behavior of the furnace.The model is able to accurately predict the spatial and temporal distribution of the temperature at each point of the furnace to control the firing of the refractory product so that the final product is of good quality in terms of resistance and hardness.In addition,the power consumed by the prototype must be optimized in order to reduce energy and environmental consumption.In particular,this efficient technology has allowed us to save 83% of energy used in the traditional furnace and to reduce 87.36% of the relative carbon dioxide emission.The simulation of the mathematical model made it possible to compare the numerical results with the experimental measurements obtained by the prototype as well as to validate the model and to adjust the heat transfer parameters.

Numerical analysis of the return flow solar air heater(RF-SAH)with assimilation of V-type artificial roughness

A novel design of Return Flow Solar Air Heater(RFSAH)with different arrangements of baffles especially V-Type Artificial roughness is simulated and numerically analyzed with energy balance equations.To enhance the effectiveness of baffles,numerous studies have been conducted.The performance of the RFSAH is studied in terms of thermal efficiency,thermo-hydraulic efficiency,and optimization of baffle parameters.Maximum Thermal efficiency and thermo-hydraulic efficiency are found in RFSAH with baffle on both sides of the absorber plate and mass flow rate above 0.2kg/s.Sensitivity analysis of the influencing parameters is carried out and reported the best performance of the system on selective geometrical parameters(ψ=0.7,β=20%,e/H=1,p/e=0.8,α=60°).The results obtained from the present model are validated with the published experimental results and have been found in quite reasonable agreement with an average error of 16.45%.Thermal and Thermohydraulic efficiency of RFSAH with a baffle on both sides of the absorber plate is maximum among baffles below,above,and on both sides of the absorber plate.It is observed that the thermal efficiency of RFSAH is greater than SF-SAH.The proposed optimum baffles roughness is suggested to increase the air upholding time period for more efficient output.

Prediction and validation of diffusive uptake rates for indoor volatile organic compounds in axial passive samplers

The diffusive uptake rate is essential for using passive samplers to measure indoor volatile organic compounds(VOCs).The traditional theoretical model of passive samplers requires available regression formulas of uptake rates and physicochemical properties of adsorbents to predict the uptake rate.However,it is difficult to obtain the uptake rates of different VOCs under different sampling periods,and it is also difficult to obtain the physical parameters of adsorbents accurately and effectively.This study provides a reliable numerical prediction method of diffusive uptake rates of VOCs.The modeling was based on the standard automated thermal desorption(ATD)tubes packed with Tenax TA and the mass transfer process during adsorption.The experimental determinations of toluene uptake rate are carried out to verify the prediction model.Diffusive uptake rates of typical indoor VOCs are obtained from the literature to calibrate the key apparent parameters in the model by statistical regression fitting.The predicted model can provide the VOC diffusive uptake rates under different sampling duration with an average deviation of less than 5%.This study can provide the basis for fast and accurate prediction of diffusive uptake rates for various VOC pollutants in built environments.

Dynamic Shading and Glazing Technologies:Improve Energy,Visual,and Thermal Performance

Conventional static glazing sometimes has poor performance in energy,visual,and thermal aspects.In this thesis,a series of simulations of an office building were done to compare the performance of conventional static glazing,exterior static and kinetic shades,dynamic glazing,and dynamic glazing working together with static or kinetic shades as a combinatorial system.This thesis introduced a method for designers to make decisions among multiple shading and glazing options.A scoring system was developed to evaluate the overall performance.Energy,visual and thermal performance all had the same weight.Energy uses included annual Energy Use Intensity(EUI)and peak cooling loads of the hottest day of the year.Visual performance included maximizing daylight and minimizing glare on the fall equinox day.Thermal performance included Predicted Mean Vote(PMV)Index which determined thermal comfort based on occupants’sensations and Predicted Percentage of Dissatisfied(PPD)which indicated the levels of thermal discomfort situations.The prediction was that the combination of dynamic glazing and kinetic shade would have better comprehensive performance and earn higher scores than other options.

Measurement and analysis of airtightness safeguard measures for typical ultra-low energy buildings

Zero-energy buildings constitute an effective means of reducing urban carbon emissions.High airtightness,a typical characteristic of zero-energy building,is closely related to the building’s air infiltration and has a signifi-cant impact on the performance of the building envelope,indoor air quality,building energy consumption,and efficient operation of air-conditioning systems.However,thus far,systematic developments in high-airtightness assurance technologies remain scarce.Most existing studies have tested the airtightness of buildings and typical building components;however,in-depth analyses into the formation of infiltration have not been reported.There-fore,for realizing zero-energy buildings,ensuring airtightness is an urgent problem that needs to be addressed.Accordingly,in this study,based on several building airtightness measurement studies,the typical air leakage paths in buildings were summarized,and the causes of typical air leakage components in buildings were further analysed by tracing construction processes.Moreover,targeted measures for airtightness in buildings were estab-lished and applied to practical cases.Lastly,the resulting improved building airtightness was measured and the results show that the airtightness of the measured ultra-low energy consumption buildings ranges from 0.13 h^(−1)to 0.57 h^(−1),with a mean value of 0.32 h^(−1).The effectiveness of the airtightness safeguard measures was verified.This study serves as a basis for the assumption of the air leakage path distribution when simulating building air infiltration and also provides a design reference for improving the construction technologies and airtightness of buildings.

Advances in ground heat exchangers for space heating and cooling:Review and perspectives

As a renewable energy source,geothermal energy has been widely used to provide space heating and cooling for buildings.The thermal performance of ground heat exchanger(GHE)is significant for the operating efficiency of the ground source heat pump(GSHP)systems.This paper presents a comprehensive review of developments and advances of three kinds of GHE,including vertical borehole GHE(VBGHE),Pile GHE(PGHE),and deep borehole GHE(DBGHE)which are currently popular in larger GSHP systems.Firstly,analytical models proposed to ana-lyze heat transfer process of VBGHE with different geological conditions are summarized,such as homogenous or heterogeneous ground,with or without groundwater advection.Numerical and short-time step models and measures to improve GHE thermal performance are also reviewed.Secondly,a summary of research advances in PGHE is provided,which includes the heat transfer models of PGHE,the effects of geometric structure,oper-ation modes,pile spacing,use of phase change material(PCM),thermal properties of PCM,thermo-mechanical behavior and/or thermal performance of PGHE.The effects of groundwater flow direction and velocity on PGHE are also summarized in brief.Lastly,models of three kinds of DBGHEs,i.e.,deep coaxial GHE(DCGHE),deep U-bend GHE(DUGHE)and super-long gravity heat pipe(SLGHP),are reviewed.The physical bases of the dif-ferent analytical models are elaborated and also their advantages and disadvantages are described.Advances in numerical modelling and improving numerical model calculation speed of DCBHE,DCBHE array,and DUBHE are summarized.The review provides a meaningful reference for the further study of GHEs.

Energy characterization of forced ventilated Photovoltaic-DSF system in hot summer of composite climate

Performance of Photovoltaic-double skin façade(Photovoltaic-DSF)system in summer has been critical.Owing to high solar ingress,cooling requirement of a building significantly increases.Photovoltaic-DSF system provides a shield and controls the heat gain through fenestration in the interior spaces.In the present article,mathematical correlations are developed for energy characterization of forced-ventilated Photovoltaic-DSF system in India’s hot summer zone i.e.Jaipur.The Photovoltaic-DSF system has been installed and monitored for Jaipur’s summer months(May to July).L25 Orthogonal array of design parameters(air cavity thickness,air velocity,and PV panel’s transparency)and their respective levels have been developed using Taguchi design to perform experiments.Based on experimental results,multiple linear regression has been used to forecast solar heat gain coefficient,PVs electrical power and daylighting illuminance indoors as function of design factors.The statistical significance of mathematical relationships is sorted by variance analysis,which is found to be in good accord with field measurements(R2>0.90).The proposed correlations are pragmatic in designing Photovoltaic-DSF systems for hot summer conditions.The Photovoltaic-DSF system with 30%transmittance and air velocity of 5 metres per second in 200 mm air cavity thickness achieved maximum energy performance in hot summers.

Assessment of micro-scale heat exchangers efficiency using lattice Boltzmann method and design of experiments

The study is focused on the use of nanofluids in a micro-open tall cavity,which is a type of micro heat exchanger(MHE).The cavity is heated from the bottom sidewall in a sinusoidal pattern,and the effects of four input parameters(Ra,Ha,Kn,and Vf)on heat transfer and irreversibility are investigated using numerical simulations based on Lattice Boltzmann Method(LBM).The findings of the study suggest that the local heat transfer on the bottom sidewall is strongly influenced by Ra and Ha,while the surface distribution of entropy generation is mainly dependent on Kn.The study also shows that the optimization of the magnitude and wavelength of the sinusoidal temperature can improve both local heat transfer and surface distribution of entropy generation.The results of the study provide valuable insights into the design of micro heat exchangers and suggest that the optimization of micro-porous geometries using DOE could lead to increased energy efficiency.The study contributes to our understanding of the complex interactions between input parameters in micro heat exchangers and highlights the importance of considering multiple parameters in the design process.

Investigation of tungsten halogen lamp for possible usage as heat source for testing solar collector

Artificial sunlight is a heat source in solar collector testing,where the light intensity and ambient conditions are controlled to provide uniformity in each test.In this study,twenty-five tungsten halogen lamps of 300 W 220 V were investigated for possible usage as the heat source.The experiment was conducted by varying the height of the lamps to the light field(H_(d)).The EN-12975-2 Standard was used to control the quality of the heat source on the light field.The results showed that the average light intensity(I_(avg))decreased with an increase in Hd.When H_(d) was low,the%uniformity values(%u_(xy))met the criteria.The small light field area met the EN-12975-2 Standard better than the large area.A comparison of solar collector testing under a heat source from tungsten halogen lamps and natural sunlight was subsequently performed,and the experimental results were in accordance with the EN-12975-2 Standard.A single-channel flat plate solar collector was used as the test device and air was used as the working fluid.The experimental results revealed that the solar collector thermal efficiency(η_(avg))trends were similar for both the heat source from tungsten halogen lamps and natural sunlight testing.A low%u_(xy) resulted in a𝜂avg that was more similar to that of natural sunlight.

Estimating cooling loads of Indian residences using building geometry data and multiple linear regression

International Energy Agency(IEA)predicts India’s AC stock will reach 1144 million units by 2050,making it the second largest ACs holder globally.Studies on the effect of building geometry on cooling load reduction are primarily focused on material and envelope specifications.However,studies on building morphological parame-ters in the Indian context are scarce.Therefore,this research quantifies the effect of four morphology predictors,namely,FL(floor number),ESA(exposed surface area),CZB(conditioned zones per building),and CZF(con-ditioned zones per floor)on cooling load in 75 dominant residential built forms of Navi Mumbai.The selected buildings are simulated using the Rhinoceros 6 tool with the energy plus plugin.Despite having the same sim-ulation inputs,envelope parameters,and conditioned volume,the results indicated a 90%variation between the compact and loosely designed forms.Multiple Linear Regression shows that the four predictors explain 78%(R2=0.78)of variation in the cooling load.It is observed that tall buildings show greater efficiency in cooling load reduction due to lesser CZF values.Also,an increase in CZB and a decrease in ESA significantly reduce the mean cooling load due to compactness and wall sharing,respectively.

Time series analysis model for forecasting unsteady electric load in buildings

Accurate and reliable load forecasting is crucial for ensuring the security and stability of the power grid.This paper proposes a combined prediction method based on Empirical Wavelet Transform(EWT)and Autoformer time series prediction model for the non-stationary and non-linear time series of electric load.The original sequence is first decomposed by EWT to obtain a set of stable subsequences,and then the Autoformer time series prediction model is used to predict each subsequence.Finally,the prediction results of each subsequence are combined to obtain the final prediction results.The proposed EWT-Autoformer prediction model is applied to an electric load example,and the experimental results are compared with the Recurrent Neural Network(RNN)method,Long Short-Term Memory(LSTM)method,and Informer method under the same conditions.The experimental results indicate that compared to LSTM,the method proposed in the paper has an R2 improvement of 9–20 percentage points,an improvement of 6–8 percentage points compared to RNN,an improvement of 3–7 percentage points compared to Informer,and an improvement of 2–3 percentage points compared to Autoformer.In addition,the RMSE and MAE are also significantly lower than other models.

Analysing light pipe two-side aperture in dense population housing to improve the visual and thermal condition

Daylight is one of the essential criteria for decent housing.However,in an urban kampung,access to daylight is limited due to the high-density population in urban Kampung Kota.This study aims to improve the visual and thermal conditions by modifying the aperture of the light pipe based on visual daylight distribution,illuminance level,and thermal conditions in multi-storey houses in hot-humid climates.Light pipe was developed experi-mentally through the simulation model to investigate the performance and impact on the visual and thermal conditions like operational temperature and RH in densely populated Kampung Kota without vertical apertures.According to the results,the two-aperture light pipe residential space enhanced the current situation but did not fulfill the standards.The two aperture light pipe visually improved daylight distribution by 1.05%-31.36%and illumination level by 8.4%-14.8%.We also found that light pipe also impacts thermal conditions with a 10.92%RH reduction while at the same time increasing temperature up to 10.57%.Therefore,it can be concluded that a two-aperture light pipe has the potential to be applied to actual conditions in hot-humid climates.

Investigation of a falling film tube bank heat exchanger with baffle design for water recovery applications

Shell and tube heat exchangers(STHE)are essential thermal equipment and widely used in daily life.A novel thermosyphon system called falling-film thermosyphon(FFTS)is introduced and integrated into STHE system,resulting in a better thermal performance.In this study,a rectangular solid tube bank of FFTS bundles with a baffle design is studied.The numerical simulation for heat and mass transfer of the FFTS heat exchanger is developed to predict the condensation rate of the vapor in the flue gas,and a lab-scale prototype is also built up in COMSOL.The prediction is validated with the experimental data from references,and the model’s accuracy is verified within 10%-12%error.Also,the Non-dominated Sorting Genetic Algorithm,version 2(NSGA-II)is implemented to improve the thermal performance of rectangular tube banks in this paper.Several parameters,e.g.,baffle number,tube number,and tube space,are optimized.As a result,compact configurations with more baffles are preferred to enhance the performance associated with a high-pressure drop correspondingly.The optimized layout for the lab-scale prototype can increase by 18 to 32%condensation with a pressure loss of less than 200 Pa.