Solar Cooling Alternatives for Residential Houses

The energy consumption rate of non-OECD(non-Organisation for Economic Co-operation and Development)countries rises about 2.3 percent per year as compared to the OECD countries which is 0.6 percent.If developing countries use energy efficient technology and integrate renewable energy systems in the new building their carbon dioxide emission rate reduces by 25 to 44 percent.However,even now,renewable energy integrated buildings are hardly considered while constructing them.This paper focuses on the study of solar cooling system options for residential house of Bahir Dar city,in Ethiopia.To meet the demand of housing in the city,different types of apartments and villa houses are under construction.For the analysis case study was made focusing on two types of residential houses,condominium apartment and Impact Real Estate Villa house.Simulation results of IDA ICE software show that the average operative temperatures and cooling loads for condominium apartment and Real-estate Vila are 31.8℃ and 30.7℃,5.53 kW and 5.73 kW respectively.Most of the residences are not satisfied at this operating temperature.There are different types of solar cooling systems.Solar sorption cooling systems are commonly used which can also be classified into absorption,adsorption and desiccant cooling systems.Solar adsorption cooling systems are easy to manufacture locally as compared to solar absorption cooling systems.They do not have moving parts.Some of the working medium pairs used in adsorption cooling system are:activated carbon/ammonia,silica gel/water,zeolite/water.Adsorption chillier with silica gel/water as a working pair was selected since it can operate at regeneration/desorption temperature as low as 45℃ coming from flat plate collectors.At 75℃ regeneration temperature,the system delivers 9℃ chilled water temperature.From cooling load simulation result direct solar irradiation is the highest source of cooling load for both houses.This gives an opportunity for passive solar cooling technology.

Correlation Study of Operational Data and System Performance of District Cooling System with Ice Storage

The district cooling system (DCS) with ice storage can reduce the peak electricity demand of the business district buildings it serves, improve system efficiency, and lower operational costs. This study utilizes a monitoring and control platform for DCS with ice storage to analyze historical parameter values related to system operation and executed operations. We assess the distribution of cooling loads among various devices within the DCS, identify operational characteristics of the system through correlation analysis and principal component analysis (PCA), and subsequently determine key parameters affecting changes in cooling loads. Accurate forecasting of cooling loads is crucial for determining optimal control strategies. The research process can be summarized briefly as follows: data preprocessing, parameter analysis, parameter selection, and validation of load forecasting performance. The study reveals that while individual devices in the system perform well, there is considerable room for improving overall system efficiency. Six principal components have been identified as input parameters for the cold load forecasting model, with each of these components having eigenvalues greater than 1 and contributing to an accumulated variance of 87.26%, and during the dimensionality reduction process, we obtained a confidence ellipse with a 95% confidence interval. Regarding cooling load forecasting, the Relative Absolute Error (RAE) value of the light gradient boosting machine (lightGBM) algorithm is 3.62%, Relative Root Mean Square Error (RRMSE) is 42.75%, and R-squared value (R2) is 92.96%, indicating superior forecasting performance compared to other commonly used cooling load forecasting algorithms. This research provides valuable insights and auxiliary guidance for data analysis and optimizing operations in practical engineering applications. .

A Deep Neural Network Coordination Model for Electric Heating and Cooling Loads Based on IoT Data

As the ubiquitous electric power internet of things(UEPIoT)evolves and IoT data increases,traditional scheduling modes for load dispatch centers have yielded a variety of chal-lenges such as calculation of real-time optimization,extraction of time-varying characteristics and formulation of coordinated scheduling strategy for capacity optimization of electric heating and cooling loads.In this paper,a deep neural network coor-dination model for electric heating and cooling loads based on the situation awareness(SA)of thermostatically controlled loads(TCLs)is proposed.First,a sliding window is used to adaptively preprocess the IoT node data with uncertainty.According to personal thermal comfort(PTC)and peak shaving contribution(PSC),a dynamic model for loads is proposed;meanwhile,personalized behavior and consumer psychology are integrated into a flexible regulation model of TCLs.Then,a deep Q-network(DQN)-based approach,using the thermal comfort and electricity cost as the comprehensive reward function,is proposed to solve the sequential decision problem.Finally,the simulation model is designed to support the validity of the deep neural network coordination model for electric heating and cooling loads,by using UEPIoT intelligent dispatching system data.The case study demonstrates that the proposed method can efficiently manage coordination with large-scale electric heating and cooling loads.

Regression tree ensemble learning-based prediction of the heating and cooling loads of residential buildings

Building energy consumption is heavily dependent on its heating load(HL)and cooling load(CL).Therefore,an efficient building demand forecast is critical for ensuring energy savings and improving the operating efficacy of the heating,ventilation,and air conditioning(HVAC)system.Modern and specialized energy-efficient building modeling technologies may offer a fair estimate of the influence of different construction methods.However,deploying these tools could be time-consuming and complex for the user.Thus,in this article,an ensemble model based on decision trees and the least square-boosting(LS-boosting)algorithm known as the regression tree ensemble(RTE)is proposed for the accurate prediction of HL and CL.The hyper parameters of the RTE are optimized by shuffled frog leaping optimization(SFLA),which leads to SRTE.Stepwise regression(STR)and Gaussian process regression(GPR)based on different kernel functions are also designed for comparison purposes.Results demonstrate that the value of root mean squared error is reduced by 37%–68%and 30%–41%for HL and CL of residential buildings,respectively,by the proposed SRTE in comparison to other models.Furthermore,the findings from the real dataset support the proposed model’s effectiveness in predicting HVAC energy usage.It can be concluded that the proposed SRTE is more effective and accurate than other methods for predicting the energy consumption of HVAC systems.

Cooling load characteristics of indoor spaces conditioned by decoupled radiant cooling unit with low radiant temperature

Decoupled radiant cooling units(DRCUs)are capable of increasing the cooling capacity without increasing condensation risks even using a much lower cooling temperature than conventional radiant cooling units(CRCUs).However,it is unclear whether DRCUs using low radiant cooling temperature will increase the cooling load of the conditioned indoor spaces.In this study,the cooling load characteristics of a thermal chamber conditioned by a DRCU was investigated through developing a steady-state analysis model suitable for both DRCUs and CRCUs.The total/radiative heat flux,as well as the heat exchange with a thermal manikin and walls were analysed under different surface temperatures of DRCUs.The effect of the emissivity of the thermal chamber’external wall on the cooling load was also investigated.Results indicated that the cooling load under the DRCU was slightly smaller than that under the CRCU when the same operative environment was created.Decreasing the infrared emissivity of the exterior wall’s inner surface could lead to a significant decrease in the cooling load for both the DRCU and CRCU.By decreasing the wall emissivity from 0.9 to 0.1,the total cooling load of the DRCU can be decreased by 8.4%and the heat gain of the exterior wall decreased by 21.6%.This study serves as a reference for developing the analysis model and understanding the load characteristics when DRCUs are used to create the thermal environment for indoor spaces.

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest energy.Therefore,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become paramount.In this vein,efforts have been made to predict the HL and CL using a univariate approach.However,this approach necessitates two models for learning HL and CL,requiring more computational time.Moreover,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware requirement.In this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D CNN.Considering the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and CL.As the 1D data are not affected by excessive parameters,the pooling layer is not applied in this implementation.Besides,the use of pooling has been questioned by recent studies.The performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

Optimal Scheduling of Air Conditioners for Energy Efficiency

Energy saving is one of the most important research hotspots, by which operational expenditure and CO2 emission can be reduced. Optimal cooling capacity scheduling in addition to temperature control can improve energy efficiency. The main contribution of this work is modeling the telecommunication building for the fabric cooling load to schedule the operation of air conditioners. The time series data of the fabric cooling load of the building envelope is taken by simulation by using Energy Plus, Building Control Virtual Test Bed （BCVTB）, and Matlab. This pre-computed data and other internal thermal loads are used for scheduling in air conditioners. Energy savings obtained for the whole year are about 4% to 6% by simulation and the field study, respectively.

Optimal Thermal Insulation Thickness in Isolated Air-Conditioned Buildings and Economic Analysis

The removal building heat load and electrical power consumption by air conditioning system are proportional to the outside conditions and solar radiation intensity. Building construction materials has substantial effects on the transmission heat through outer walls, ceiling and glazing windows. Good thermal isolation for buildings is important to reduce the transmitted heat and consumed power. The buildings models are constructed from common materials with 0 - 16 cm of thermal insulation thickness in the outer walls and ceilings, and double-layers glazing windows. The building heat loads were calculated for two types of walls and ceiling with and without thermal insulation. The cooling load temperature difference method, CLTD, was used to estimate the building heat load during a 24-hour each day throughout spring, summer, autumn and winter seasons. The annual cooling degree-day, CDD was used to estimate the optimal thermal insulation thickness and payback period with including the solar radiation effect on the outer walls surfaces. The average saved energy percentage in summer, spring, autumn and winter are 35.5%, 32.8%, 33.2% and 30.7% respectively, and average yearly saved energy is about of 33.5%. The optimal thermal insulation thickness was obtained between 7 - 12 cm and payback period of 20 - 30 month for some Egyptian Cities according to the Latitude and annual degree-days.

Comparison of space cooling/heating load under non-uniform indoor environment with convective heat gain/loss from envelope

The indoor parameters are generally non-uniform distributed.Consequently,it is important to study the space cooling/heating load oriented to local requirements.Though the influence of indoor set point,heat sources,and ambient temperature of convective thermal boundary on cooling/heating load has been investigated in the uniform environment in previous research,the influence of these factors,particularly the convective heat gain/loss through a building envelope,on cooling/heating load of non-uniform environment has not yet been investigated.Therefore,based on the explicit expression of indoor temperature under the convective boundary condition,the expression of space cooling/heating load with convective heat transfer from the building envelope is derived and compared through case studies.The results can be summarized as follows.(1)The convective heat transferred through the building envelope is significantly related to the airflow patterns:the heating load in the case with ceiling supply air,where the supply air has a smaller contribution to the local zone,is 24%higher than that in the case with bottom supply air.(2)The degree of influence from each thermal boundary to the local zone of space cooling cases is close to that of a uniform environment,while the influence of each factor,particularly that of supply air,is non-uniformly distributed in space heating.(3)It is possible to enhance the influence of supply air and heat source with a reasonable airflow pattern to reduce the space heating load.In general,the findings of this study can be used to guide the energy savings of rooms with non-uniform environments for space cooling/heating.

Energy and exergy analyses and optimizations for two-stage TEC driven by two-stage TEG with Thomson effect

Based on the non-equilibrium thermodynamics and energy and exergy analyses,a thermodynamic model of two-stage thermoelectric(TE)cooler(TTEC)driven by two-stage TE generator(TTEG)(TTEG-TTEC)combined TE device is established with involving Thomson effect by fitting method of variable physical parameters of TE materials.Taking total number of TE elements as constraint,influences of number distributions of TE elements on three device performance indictors,that is,cooling load,maximum COP and maximum exergetic efficiency,are analyzed.Three number distributions of TE elements are optimized with three maximum performance indictors as the objectives,respectively.Influences of hot-junction temperature of TTEG and coldjunction temperature of TTEC on optimization results are analyzed,and difference between optimization results corresponding to three performance indicators are studied.Optimal performance intervals and optimal variable intervals are provided.Influences of Thomson effect on three general performance indicators,three optimal performance indicators and optimal variables are comparatively discussed.Thomson effect reduces three general performance indicators and three optimal performance indicators of device.When hot-and cold-junction temperatures of TTEG and TTEC are 450,305,325 and 295 K,respectively,Thomson effect reduced maximum cooling load,maximum COP and maximum exergetic efficiency from 9.528 W,9.043×10^(-2)and2.552%to 6.651 W,6.286×10^(-2)and 1.752%,respectively.

An integrated computational method for calculating dynamic thermal bridges of building facades in tropical countries

Identifying thermal bridges on building façades has been a great challenge for architects,especially during the conceptual design stage.This is not only due to the complexity of parameters when calculating thermal bridges,but also lack of feature integration between building energy simulation(BES)tools and the actual building conditions.For example,existing BES tools predominantly calculate thermal bridges only in steady state without considering the temperature dynamic behaviour of building outdoors.Consequently,relevant features such as thermal delay,decrement factor,and operative temperature are often neglected,and this can lead to miscalculation of energy consumption.This study then proposes an integrated method to calculate dynamic thermal bridges under transient conditions by incorporating field observations and computational simulations of thermal bridges.More specifically,the proposed method employs several measurement tools such as HOBO data logger to record the actual conditions of indoor and outdoor room temperature and thermal cameras to identify the surface temperature of selected building junctions.The actual datasets are then integrated with the simulation workflow developed in BES tools.This study ultimately enables architects not only to identify potential thermal bridges on existing building façades but also to support material and geometric exploration in early design phase.

Multi-objective method of selecting performance-based local climatic zones using binomial logistic regression in warm and humid climate

Urban agglomeration is a serious concern due to its high energy usage and impact on the local climate.Developing countries strive to determine the development path to optimize energy usage.The present study aims to examine the local climatic zones(LCZs)performance in warm and humid climate through a multi-objective approach for the residential sector.The performance is assessed by evaluating the urban microclimate and cooling load consumption for both summer and winter months using binomial logistic regression.The study concludes that LCZ 2_(3)(compact mid-rise with open low-rise)and LCZ 6_(B)(open low-rise with scattered trees)perform better for 80%and 50%of total hours in warm and humid climate.It also proves the presence of significant performance differences between mid-rise and low-rise zones.The intra-zonal differences between the climatic variables are higher than the inter-zonal differences due to the impact of land surface temperature(LST).The high aspect ratio and low sky view factor of LCZ 2_(3) help the residents in that morphology in enhancing better thermal comfort and reducing cooling load consumption.The present study contributes to building regulation policymakers by providing information on the suitable morphology for warm and humid climate.

Evaluating the improvement effect of low-energy strategies on the summer indoor thermal environment and cooling energy consumption in a library building:A case study in a hot-humid and less-windy city of China

Public buildings such as libraries consume a vast amount of cooling energy for maintaining a comfortable and stable indoor environment in summer,especially in the hot-humid climate.This study used a case study approach to discuss the effect of low-energy strategies that can be applied to improve indoor thermal environment and cooling energy consumption of library buildings in hot and humid cities like Nanning City(a southern city,China).The use of cooling window shutters(a shutter with the effects of shading and evaporative cooling)and ceiling fans for generating airflow was considered as applicable energy-saving measures in this study,and a university library was selected as the study building in which the two energy-saving measures were employed.The SET*and annual cooling load before and after the adoption of the proposed measures were quantitatively investigated with a building energy consumption simulation software(DesignBuilder).Simulation results showed that the daytime SET*values can be reduced by 3.0℃and 4.5℃respectively on a typical summer day after the use of the cooling shutters and ceiling fans.Moreover,the cooling loads can also be decreased by 8.4%and 16.6%respectively.Particularly,the combination of these two measures enabled the daytime SET*value and annual cooling load lower by 7.0℃and 60.8%respectively.

Numerical simulation study on the hygrothermal performance of building exterior walls under dynamic wind-driven rain condition

Wind-driven rain(WDR)has a significant influence on the hygrothermal performance,durability,and energy consumption of building components.The calculation of WDR loads using semi-empirical models has been incorporated into the boundary conditions of coupled heat and moisture transfer models.However,prior research often relied on fixed WDR absorption ratio,which fail to accurately capture the water absorption characteristics of porous building materials under rainfall scenarios.Therefore,this study aims to investigate the coupled heat and moisture transfer of exterior walls under dynamic WDR boundary conditions,utilizing an empirically obtained WDR absorption ratio model based on field measurements.The developed coupled heat and moisture transfer model is validated against the HAMSTAD project.The findings reveal that the total WDR flux calculated with the dynamic WDR boundary is lower than that obtained with the fixed WDR boundary,with greater disparities observed in orientations experiencing higher WDR loads.The variations in moisture flow significantly impact the surface temperature and relative humidity of the walls,influencing the calculation of cooling and heating loads by different models.Compared to the transient heat transfer model,the coupled heat and moisture transfer model incorporating dynamic WDR boundary exhibits maximum increases of 17.6%and 16.2%in cooling and heating loads,respectively.The dynamic WDR boundary conditions provide more precise numerical values for surface moisture flux,offering valuable insights for the thermal design of building enclosures and load calculations for HVAC systems.

Thermal-structural analysis of regeneratively-cooled thrust chamber wall in reusable LOX/Methane rocket engines

To predict the thermal and structural responses of the thrust chamber wall under cyclic work,a 3-D fluid-structural coupling computational methodology is developed.The thermal and mechanical loads are determined by a validated 3-D finite volume fluid-thermal coupling computational method.With the specified loads,the nonlinear thermal-structural finite element analysis is applied to obtaining the 3-D thermal and structural responses.The Chaboche nonlinear kinematic hardening model calibrated by experimental data is adopted to predict the cyclic plastic behavior of the inner wall.The methodology is further applied to the thrust chamber of LOX/Methane rocket engines.The results show that both the maximum temperature at hot run phase and the maximum circumferential residual strain of the inner wall appear at the convergent part of the chamber.Structural analysis for multiple work cycles reveals that the failure of the inner wall may be controlled by the low-cycle fatigue when the Chaboche model parameter c3= 0,and the damage caused by the thermal-mechanical ratcheting of the inner wall cannot be ignored when c3〉 0.The results of sensitivity analysis indicate that mechanical loads have a strong influence on the strains in the inner wall.