A modified method of calculating the heating load for residential buildings

Fouda, A.; Melikyan, Zohrab; Marrakchi, M.; Elattar, H.F.

doi:10.1016/j.enbuild.2014.01.047

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…With peak heat gain occurring at 11 a.m. in the morning and 5 p.m. in the afternoon the pattern of heat gain in these two rooms is same. Figure (6)(7) shows the same trend for first day of taken month. There isn't much that can be done to reduce heat gain and thereby regulate the power usage of the air conditioning system, Since the heat gain is dependent on external heat gain and the internal components of heat gain are constant,.…”

Section: Discussionsupporting

confidence: 62%

“…He reasoned that aftereffects of two calculations were minimal extraordinary and consistent estimation strategy was more basic & productive. A. Fouda et al [6] predicted an altered policy for figuring the cooling load for remote structures. The result of this technique were contrasted and the ASHRAE principles and tracked down that the outcomes come from this strategy were more precise and successful.…”

Section: Introductionmentioning

confidence: 99%

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Cooling Load Estimation of a Multi-Storey Building: A Heat Transfer Approach

Alam¹,

Amir²,

Mansoor³

et al. 2021

JUSST

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The key objective of this work is to maintain the pre-determined inside conditions & to establish thermal equilibrium the prime rate at which heat needs to be detached from the space. Nowadays one of the most serious problems is environmental issues. For this problem, energy utilization by buildings and enterprises are responsible. Markets, Residential houses, commercial buildings, industry, and Infrastructure consume approximately 72% of the world’s energy. Roughly 60 % of a building’s total energy necessity is distributed to the plant of air-conditioning installed in a big complex or building that is air acclimatized. To limit energy utilization, accurate prediction of the cooling load are important. The elementary heat transfer concepts are used to manually calculate the cooling load of a multi-storey building. This method is derived from CLTD technique of cooling load estimation. We estimate the cooling load at the extreme conditions. So, we have taken the outside conditions as relative humidity 54% and 450C DBT for the month of May during summer. The average outside air velocity during this period is 1.67 m/s. The significance of this work is to show that, actual cooling load prediction results in less capital cost, investment and energy consumed. Thus, accuracy should be paramount when load calculation is being performed.

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Section: Discussionsupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Cooling Load Estimation of a Multi-Storey Building: A Heat Transfer Approach

Alam¹,

Amir²,

Mansoor³

et al. 2021

JUSST

View full text Add to dashboard Cite

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“…Solar radiation and heat demand increase caused by a higher wind speed were taken into consideration in the prediction of heat supply for a single building [8]. In seasonal heating load calculations [9], it was important to carry out climatologic investigations and develop empirical equations for determining the total duration of daytime temperatures and solar radiation intensities.…”

Section: System Performance and Influencing Factors Of Dynamicmentioning

confidence: 99%

“…where max and min represent the maximum and minimum weight factors, respectively; iter is the current iteration; iter max is the maximum iteration. In (9), when the value gradually decreases in the search process, it meets the demand of the adaptive process for the algorithm from global optimization to local optimization. (PSO-SVR).…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

Wang

2016

Mathematical Problems in Engineering

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We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR) model-optimized particle swarm optimization (PSO) algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR), and artificial neural network (ANN) through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

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