In this paper, we study the influence ofeco materials for roof insulation and fiber-reinforced mortar coatings on cooling loads of a home in dry tropical climate. The walls of the house are made of cinderblock or late...In this paper, we study the influence ofeco materials for roof insulation and fiber-reinforced mortar coatings on cooling loads of a home in dry tropical climate. The walls of the house are made of cinderblock or laterite and the insulating material of a roof panel is made with lime （24%）, cement （6%）, water （50%） of vegetable fibers hibiscus sabdariffa （16%）, tree widespread in Burkina Faso and sugar cane bagasse （4%）. This panel roof insulation and the fiber-reinforced mortar were characterized at the Laboratory of Physics and Chemistry of the environment by the hot plate method. The building is modeled in TRNSYS using climate data from the city of Ouagadougou. The results obtained show that in the warmer months of the year, that is to say in March and April, the relative differences between heat gains the configurations ＂breeze block-coating mortar and roof not insulated＂ and ＂laterite- fiber-reinforced mortar coating and insulated roof＇ vary between 15.6% and 16.8%. The configuration ＂laterite-fiber-reinforced mortar coating and insulated roof allows a reduction of annual heat gains of 15.5% compared to the configuration ＂breeze block-coating mortar and roof not insulated＂.展开更多
The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads...The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.展开更多
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 countr...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.展开更多
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 mon...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 (R<sup>2</sup>) 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. .展开更多
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 o...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.展开更多
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...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.展开更多
The energy saving performance of energy efficient windows has strong dependence on window direction. Transmitted insolation level definitely affected the cooling and heating load. Simple simulation on the decrement of...The energy saving performance of energy efficient windows has strong dependence on window direction. Transmitted insolation level definitely affected the cooling and heating load. Simple simulation on the decrement of cooling load and the increment of heating load of a shading window compared with those of a transparent window show the prospect of energy saving effect clearly.From southeastward to southwestward, shading window even enlarges total heating and cooling loads when the thermal transmission is the same. However, if the shading coefficient of window is switched between summer and winter, total cooling and heating load can be reduced. This result clarifies the importance of ＂smart window＂.展开更多
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 ar...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.展开更多
A building model with radiant cooling system was established and the cooling load, indoor temperature, surface temperature of the wails and other parameters in non-cooling and radiant cooling room were calculated by T...A building model with radiant cooling system was established and the cooling load, indoor temperature, surface temperature of the wails and other parameters in non-cooling and radiant cooling room were calculated by TRNSYS. The comparative analysis of the characteristics of attenuation and delay proves that the operation of radiant cooling system increases the degree of temperature attenuation of the room and reduces the inner surface temperature of the wall significantly, but has little effect on the attenuation coefficient and delay time of wall heat transfer. The simulation results also show that the inner surface temperature of the walls in the radiant cooling room is much lower than that in non-cooling room in the day with the maximum cooling load, which reduces the indoor operation temperature largely, and improves the thermal comfort. Finally, according to the analysis of indoor temperature of the rooms with different operation schedules of cooling system, it can be derived that the indoor mean temperature changes with the working time of radiant cooling system, and the operation schedule can be adjusted in practice according to the actual indoor temperature to achieve the integration of energy efficiency and thermal comfort.展开更多
Cooling energy needs, for mines in Northern Ontario, are mainly driven by the mining depth and its operation. Part I of this research focusses on the thermal energy loads in deep mines as a result of the virgin rock t...Cooling energy needs, for mines in Northern Ontario, are mainly driven by the mining depth and its operation. Part I of this research focusses on the thermal energy loads in deep mines as a result of the virgin rock temperature, mining operations and climatic conditions. A breakdown of the various heat sources is outlined, for an underground mine producing 3500 tonnes per day of broken rock, taking into consideration the latent and sensible portions of that heat to properly assess the wet bulb global temperature. The resulting thermal loads indicate that cooling efforts would be needed both at surface and underground to maintain the temperature underground within the legal threshold. In winter the air might also have to be heated at surface and cooled underground, to ensure that icing does not occur in the inlet ventilation shaft-the main reason why coolin~ cannot be focussed solely at surface.展开更多
The cooling and heating load distribution of large area air-conditioned room such as “open” offices, shopping malls and waiting rooms is usually assumed to be even in air conditioning system design. However, it is n...The cooling and heating load distribution of large area air-conditioned room such as “open” offices, shopping malls and waiting rooms is usually assumed to be even in air conditioning system design. However, it is not the case in reality, and a low efficient air conditioning system results from this assumption. A simulation and analysis of the cooling load distribution of an office building in Hong Kong with TRANSYS software is provided in this paper. A typical office is divided into 13 zones for simulation, including external zone, medial zone and internal zone in the north, the south, the east and the west respectively and a central zone, instead of 4 directional zone. The result shows there is much cooling load difference between each zone, and more attention should be paid to uneven indoor cooling and heating load distribution to further guide the design.展开更多
Cooling energy needs, for mines in Northern Ontario, are mainly driven by the mining cooling technologies available and the cost to implement them in a 2500 m deep underground mine. The cooling technologies reviewed h...Cooling energy needs, for mines in Northern Ontario, are mainly driven by the mining cooling technologies available and the cost to implement them in a 2500 m deep underground mine. The cooling technologies reviewed herein include mechanical and natural cooling systems, ranging from mechanical chillers to seasonal thermal storages. The economic and operating parameters for each technology were estimated and evaluated according to the mine's energy loads. Including consideration of any combined heat and power benefits of the technology, cooling tower requirements, etc., the resulting cost of implementation for each technology could be ranked. This showed that the natural thermal storage systems and conventional chillers were the most cost-effective, mainly since the natural systems had very low operating cost and the chillers had relatively low capital costs.展开更多
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,effectiv...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.展开更多
Abstract--Vapor compression refrigeration cycle （VCC） system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based ...Abstract--Vapor compression refrigeration cycle （VCC） system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy efficient control strategy which aims at maximizing the system efficiency is proposed. Firstly, according to the mass and energy conservation law, an analysis on the nonlinear relationship between superheat and cooling load is carried out, which can produce the maximal effect on the system performance. Then a model predictive control （MPC） based controller is developed for tracking the calculated setting curve of superheat degree and pressure difference based on model identified from data which can be obtained from an experimental rig. The proposed control strategy maximizes the coefficient of performance （COP） which depends on operating conditions, in the meantime, it meets the changing demands of cooling capacity. The effectiveness of the control performance is validated on the experimental rig. Index Terms--Cooling load, model predictive control （MPC）, superheat, vapor compression refrigeration cycle （VCC）.展开更多
Indoor air quality and thermal comfort are important features of indoor environment. In this paper, a numerical simulation based on the k-ε model of CFD is used to analyze factors such as loading, exterior-protected ...Indoor air quality and thermal comfort are important features of indoor environment. In this paper, a numerical simulation based on the k-ε model of CFD is used to analyze factors such as loading, exterior-protected construction, blowing-in rate that play an important role in the temperature field and airflow field of the displacement ventilation system. Exterior-protected construction has little influence on indoor temperature distribution of displacement ventilation systems and the influence is limited only in a small area near the external wall when the indoor heat source is the main cooling load.The height of a room has little influence on indoor temperature field, and the temperature gradient of active region is basically unchanged. In the system combined with a displacement ventilation system and a cooling system, the height also has little influence. When the cooling load is high,the indoor heat source creates a strong convective plume, which will make the average indoor air age lower, the ventilation efficiency higher and the elimination of pollutant easier. Air supply rate plays an important role in displacement ventilation systems. The increase of air supply rate that can be realized by increasing the air supply velocity and enlarging the area of air inlet will increase the mass capability of the system and diminish the vertical temperature gradient. From the comparison between simulations and experiments, it is concluded that this simulation are creditable.展开更多
Crop germplasm resources are the material basis for the breeding of new varieties, and strategic resources for food production, ecological security and agricultural sustainable development. Constructing a scientific, ...Crop germplasm resources are the material basis for the breeding of new varieties, and strategic resources for food production, ecological security and agricultural sustainable development. Constructing a scientific, environmental-friendly and energy-saving medium-term genebank is essential for preserving crop germplasm resources. However, the construction of medium-term genebank involves a wide range of subjects but lacks unified standard, which might result in many difficulties in the process of construction and application. According to the key parameters of refrigeration system for medium-term genebank, the cooling load was calculated and key system schemes were determined in this paper. Based on the calculation results and designed schemes, the equipment selection was discussed and the standards for construction of bank and monitoring system were proposed with the aim to provide references for germplasm genebank design and equipment selection.展开更多
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...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.展开更多
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 effe...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, <em>CLTD</em>, 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, <em>CDD</em> 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.展开更多
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 establ...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.展开更多
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 therma...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.展开更多
文摘In this paper, we study the influence ofeco materials for roof insulation and fiber-reinforced mortar coatings on cooling loads of a home in dry tropical climate. The walls of the house are made of cinderblock or laterite and the insulating material of a roof panel is made with lime （24%）, cement （6%）, water （50%） of vegetable fibers hibiscus sabdariffa （16%）, tree widespread in Burkina Faso and sugar cane bagasse （4%）. This panel roof insulation and the fiber-reinforced mortar were characterized at the Laboratory of Physics and Chemistry of the environment by the hot plate method. The building is modeled in TRNSYS using climate data from the city of Ouagadougou. The results obtained show that in the warmer months of the year, that is to say in March and April, the relative differences between heat gains the configurations ＂breeze block-coating mortar and roof not insulated＂ and ＂laterite- fiber-reinforced mortar coating and insulated roof＇ vary between 15.6% and 16.8%. The configuration ＂laterite-fiber-reinforced mortar coating and insulated roof allows a reduction of annual heat gains of 15.5% compared to the configuration ＂breeze block-coating mortar and roof not insulated＂.
文摘The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.
文摘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.
文摘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 (R<sup>2</sup>) 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. .
基金This project was supported by National Key Research and Development Plan(2017YFB0902100)Key Project of Liaoning Natural Science Foundation under Grant(20170520292).
文摘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.
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A2C3013687)the GIST Research Institute(GRI)grant funded by the GIST in GIST Research Project.
文摘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.
文摘The energy saving performance of energy efficient windows has strong dependence on window direction. Transmitted insolation level definitely affected the cooling and heating load. Simple simulation on the decrement of cooling load and the increment of heating load of a shading window compared with those of a transparent window show the prospect of energy saving effect clearly.From southeastward to southwestward, shading window even enlarges total heating and cooling loads when the thermal transmission is the same. However, if the shading coefficient of window is switched between summer and winter, total cooling and heating load can be reduced. This result clarifies the importance of ＂smart window＂.
文摘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.
基金Project(2010DFA72740) supported by the International Science & Technology Cooperation Program of China
文摘A building model with radiant cooling system was established and the cooling load, indoor temperature, surface temperature of the wails and other parameters in non-cooling and radiant cooling room were calculated by TRNSYS. The comparative analysis of the characteristics of attenuation and delay proves that the operation of radiant cooling system increases the degree of temperature attenuation of the room and reduces the inner surface temperature of the wall significantly, but has little effect on the attenuation coefficient and delay time of wall heat transfer. The simulation results also show that the inner surface temperature of the walls in the radiant cooling room is much lower than that in non-cooling room in the day with the maximum cooling load, which reduces the indoor operation temperature largely, and improves the thermal comfort. Finally, according to the analysis of indoor temperature of the rooms with different operation schedules of cooling system, it can be derived that the indoor mean temperature changes with the working time of radiant cooling system, and the operation schedule can be adjusted in practice according to the actual indoor temperature to achieve the integration of energy efficiency and thermal comfort.
基金CEMI (Centre for Excellence in Mining Innovation) for their funding to support this research
文摘Cooling energy needs, for mines in Northern Ontario, are mainly driven by the mining depth and its operation. Part I of this research focusses on the thermal energy loads in deep mines as a result of the virgin rock temperature, mining operations and climatic conditions. A breakdown of the various heat sources is outlined, for an underground mine producing 3500 tonnes per day of broken rock, taking into consideration the latent and sensible portions of that heat to properly assess the wet bulb global temperature. The resulting thermal loads indicate that cooling efforts would be needed both at surface and underground to maintain the temperature underground within the legal threshold. In winter the air might also have to be heated at surface and cooled underground, to ensure that icing does not occur in the inlet ventilation shaft-the main reason why coolin~ cannot be focussed solely at surface.
文摘The cooling and heating load distribution of large area air-conditioned room such as “open” offices, shopping malls and waiting rooms is usually assumed to be even in air conditioning system design. However, it is not the case in reality, and a low efficient air conditioning system results from this assumption. A simulation and analysis of the cooling load distribution of an office building in Hong Kong with TRANSYS software is provided in this paper. A typical office is divided into 13 zones for simulation, including external zone, medial zone and internal zone in the north, the south, the east and the west respectively and a central zone, instead of 4 directional zone. The result shows there is much cooling load difference between each zone, and more attention should be paid to uneven indoor cooling and heating load distribution to further guide the design.
基金CEMI (Centre for Excellence in Mining Innovation) for their funding to support this research
文摘Cooling energy needs, for mines in Northern Ontario, are mainly driven by the mining cooling technologies available and the cost to implement them in a 2500 m deep underground mine. The cooling technologies reviewed herein include mechanical and natural cooling systems, ranging from mechanical chillers to seasonal thermal storages. The economic and operating parameters for each technology were estimated and evaluated according to the mine's energy loads. Including consideration of any combined heat and power benefits of the technology, cooling tower requirements, etc., the resulting cost of implementation for each technology could be ranked. This showed that the natural thermal storage systems and conventional chillers were the most cost-effective, mainly since the natural systems had very low operating cost and the chillers had relatively low capital costs.
基金supported in part by the Institute of Information and Communications Technology Planning and Evaluation(IITP)Grant by the Korean Government Ministry of Science and ICT(MSITArtificial Intelligence Innovation Hub)under Grant 2021-0-02068in part by the NationalResearch Foundation of Korea(NRF)Grant by theKorean Government(MSIT)under Grant NRF-2021R1I1A3060565.
文摘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.
基金supported by the National Natural Science Foundation of China(61233004,61221003,61374109,61473184,61703223,61703238)the National Basic Research Program of China(973 Program)(2013CB035500)+1 种基金Shandong Provincial Natural Science Foundation of China(ZR2017BF014,ZR2017MF017)the National Research Foundation of Singapore(NRF-2011,NRF-CRP001-090)
文摘Abstract--Vapor compression refrigeration cycle （VCC） system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy efficient control strategy which aims at maximizing the system efficiency is proposed. Firstly, according to the mass and energy conservation law, an analysis on the nonlinear relationship between superheat and cooling load is carried out, which can produce the maximal effect on the system performance. Then a model predictive control （MPC） based controller is developed for tracking the calculated setting curve of superheat degree and pressure difference based on model identified from data which can be obtained from an experimental rig. The proposed control strategy maximizes the coefficient of performance （COP） which depends on operating conditions, in the meantime, it meets the changing demands of cooling capacity. The effectiveness of the control performance is validated on the experimental rig. Index Terms--Cooling load, model predictive control （MPC）, superheat, vapor compression refrigeration cycle （VCC）.
文摘Indoor air quality and thermal comfort are important features of indoor environment. In this paper, a numerical simulation based on the k-ε model of CFD is used to analyze factors such as loading, exterior-protected construction, blowing-in rate that play an important role in the temperature field and airflow field of the displacement ventilation system. Exterior-protected construction has little influence on indoor temperature distribution of displacement ventilation systems and the influence is limited only in a small area near the external wall when the indoor heat source is the main cooling load.The height of a room has little influence on indoor temperature field, and the temperature gradient of active region is basically unchanged. In the system combined with a displacement ventilation system and a cooling system, the height also has little influence. When the cooling load is high,the indoor heat source creates a strong convective plume, which will make the average indoor air age lower, the ventilation efficiency higher and the elimination of pollutant easier. Air supply rate plays an important role in displacement ventilation systems. The increase of air supply rate that can be realized by increasing the air supply velocity and enlarging the area of air inlet will increase the mass capability of the system and diminish the vertical temperature gradient. From the comparison between simulations and experiments, it is concluded that this simulation are creditable.
文摘Crop germplasm resources are the material basis for the breeding of new varieties, and strategic resources for food production, ecological security and agricultural sustainable development. Constructing a scientific, environmental-friendly and energy-saving medium-term genebank is essential for preserving crop germplasm resources. However, the construction of medium-term genebank involves a wide range of subjects but lacks unified standard, which might result in many difficulties in the process of construction and application. According to the key parameters of refrigeration system for medium-term genebank, the cooling load was calculated and key system schemes were determined in this paper. Based on the calculation results and designed schemes, the equipment selection was discussed and the standards for construction of bank and monitoring system were proposed with the aim to provide references for germplasm genebank design and equipment selection.
基金support and facilities provieded by Bharat Sanchar Nigam Limited Chennai Telephones and Department of Telecommunications,India for this study
文摘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.
文摘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, <em>CLTD</em>, 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, <em>CDD</em> 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.
基金supported by the National Natural Science Foundation of China(Grant No.52171317)。
文摘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.
基金This research is funded by Directorate of Research and Development,Universitas Indonesia under Hibah PUTI Q1 Batch 22022(NKB-1149/UN2.RST/HKP.05.00/2022)awarded to Dr.Miktha Farid Alkadri S.Ars.,M.Ars.We also thank to Dr.Eng.Arnas,ST.,M.T.,from the Department of Mechanical Engineering,Universitas Indonesia,who has provided valuable input during the research process and HTflux team who has supplied a license for thermal bridge simulation.
文摘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.
