Assessment of a modified method for determining the cooling load of residential buildings

Fouda, A.; Melikyan, Zohrab

doi:10.1016/j.energy.2010.09.022

Cited by 17 publications

(3 citation statements)

References 7 publications

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“…The reflected part is transmitted to other walls again, absorbed and reflected by the walls again, and continuously reflected and absorbed until the absorption is completed. 34,35 Therefore, the building walls possess radiation energy emitted by itself, absorbed radiation energy from other walls and reflected energy.…”

Section: Introductionmentioning

confidence: 99%

Building thermal environment solution model and radiation heat transfer calculation model based on Gebhart absorption coefficient

Xiang

Huang

2023

Advances in Mechanical Engineering

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The building walls temperatures are different, and there are radiation heat transfers between the walls, which have the characteristics of multiple reflections and multiple absorptions. The Gebhart absorption coefficient considers the radiation energy both directly absorbed by the wall and indirectly absorbed by other walls multiple times reflections. The absorption coefficient Gij is used to represent the proportion of radiation energy absorbed by surface j from the radiation energy of surface i. Through employing Gebhart absorption coefficient, the Gebhart absorption coefficient matrix is constructed based on the wall angle factors and the wall radiation emissivity. The building thermal environment solution model is established, which can calculate the indoor air temperature and wall temperature. The wall radiation heat transfer calculation model is built to calculate the net radiation heat transfer of the walls. An experimental bench was carried out to verify the above theoretical calculation results. The research results showed that the theoretical calculated values of the air temperatures of the building thermal environment solution model were basically consistent with the measured values, and the deviation was within 0.25°C. The deviation between the calculated values of the wall temperatures and the measured values was basically within 1.0°C. The error between the calculated values of the wall radiation heat transfer calculation model and the measured values varied within a small range. The Gebhart absorption coefficient is more appropriate in characterizing radiation heat transfer mechanism of the walls, and it provides a new way and thought for the analysis of radiation heat transfer on building wall.

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Section: Introductionmentioning

confidence: 99%

Building thermal environment solution model and radiation heat transfer calculation model based on Gebhart absorption coefficient

Xiang

Huang

2023

Advances in Mechanical Engineering

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“…These figures indicate the importance of building elements to be carefully selected and A/C systems to be precisely sized to minimise carbon emissions. To ensure this, heat gain assessment needs to be carried out to determine cooling loads [5]. In the calculation of cooling loads, a heat balance equation is needed that is derived from numerical models [6].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of CIBSE Admittance and ASHRAE Radiant Time Series Cooling Load Models

et al. 2022

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Due to the impacts of carbon emissions on climate change and the expected dramatic increase in global cooling demand by 2050, it is of a paramount importance that the required energy to cool buildings is accurately predicted. This ensures that equipment is appropriately sized, which ultimately reduces energy consumption and global carbon emissions. CIBSE and ASHRAE standards are both widely adopted for cooling load predictions, but they adopt different calculation methods, with CIBSE adopting admittance and ASHRAE adopting radiant time series (RTS), which produce significantly different results in cooling load. This study comparatively and qualitatively evaluates the CIBSE admittance and ASHRAE RTS cooling load models by analysing their structures and key input parameters for a mock-up building to identify inconsistencies between the two methods. There were flaws within both models that resulted in the CIBSE method underpredicting the cooling load, whereas the ASHRAE method typically overpredicting it. This resulted in a maximal average difference of over 60%. The substantial predicted cooling load difference was mainly caused by the ASHRAE RTS model, which was highly receptive to solar gains, and it consequently led to overprediction in cooling load when compared to the CIBSE admittance model.

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“…Thermal energy is the product of mass flow, temperature difference, the specific heat of the water, and time. Models for predicting the heat demand can be classified into two categories: physical models, which are developed based on energy balance according to the principle of heat transfer [5], [6]; and statistic models, which are built up based on measured data and data processing technologies [7], [9], [10], [25], [59], [60]. Evolving technologies about smart meters and smart energy network open up new opportunities for the second category as more and more energy data become available [11], [21].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Weather Conditions on District Heat System

Xie,

Ma,

Guo

2018

Preprint

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Using artificial neural network for the prediction of heat demand has attracted more and more attention. Weather conditions, such as ambient temperature, wind speed and direct solar irradiance, have been identified as key input parameters. In order to further improve the model accuracy, it is of great importance to understand the influence of different parameters. Based on an Elman neural network (ENN), this paper investigates the impact of direct solar irradiance and wind speed on predicting the heat demand of a district heating network. Results show that including wind speed can generally result in a lower overall mean absolute percentage error (MAPE) (6.43%) than including direct solar irradiance (6.47%); while including direct solar irradiance can achieve a lower maximum absolute deviation (71.8%) than including wind speed (81.53%). In addition, even though including both wind speed and direct solar irradiance shows the best overall performance (MAPE=6.35%), ENN may not always benefit from the simultaneous introduction of both wind speed and direct solar irradiance, according to MAPE in broken down ranges of heat demands (0∼150MW, 150∼300MW, 300∼450MW and >450MW).

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Assessment of a modified method for determining the cooling load of residential buildings

Cited by 17 publications

References 7 publications

Building thermal environment solution model and radiation heat transfer calculation model based on Gebhart absorption coefficient

Building thermal environment solution model and radiation heat transfer calculation model based on Gebhart absorption coefficient

Comparative Analysis of CIBSE Admittance and ASHRAE Radiant Time Series Cooling Load Models

Impacts of Weather Conditions on District Heat System

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