The intelligent control of the central air conditioning

Xu, Jian; Zhou, Jingjing

doi:10.1109/csip.2012.6308948

Cited by 3 publications

(2 citation statements)

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“…Eqs. (35)(36)(37)(38)(39)(40)(41)(42)) are formulated using nonlinear regression analysis in SPSS statics 20 software for the given data in Table 4 as follows :…”

Section: Regression Modelsmentioning

confidence: 99%

“…Several investigations (Al- Yiu and Wang, 2007) have been published concerning the mathematical modeling of heating and air-conditioning energies of buildings via statistical regression analysis. Fuzzy logic mathematical model is also utilized to predict these loads by other researchers (Costa and La Neve, 2015;Dexter and Benouarets, 1996;Fraisse and Virgone, 1997;Huang et al, 2009;Kolokotsa et al, 2006;Li et al, 2011;Lopez et al, 2004;Ngo and Dexter, 1999;Rezeka et al, 2015;Xu and Zhou, 2012). The formulation of simple mathematical models to evaluate air-conditioning and heating loads of buildings is necessary to be used in lieu of aforementioned ponderous methods.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Modeling for Prediction of Heating and Air-Conditioning Energies of Multistory Buildings in Duhok City

2018

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Present endeavor is devoted to estimate the air-conditioning and heating energies or loads of modern buildings in Duhok City, Iraq using new mathematical models. Many parameters have been considered in current modeling, namely, area of building, number of storeys and types of the common materials of the building walls. Regression analysis is performed to formulate new mathematical linear and nonlinear models for the loads. In addition, Fuzzy logic is utilized in the third model employing Sugeno's regulation. The outcomes reveal that the reasonable matching is achieved between the proposed models and mechanical engineering analytical solutions of heating and air-conditioning standards. Consequently, high correlation coefficient as more than 85% is determined between the predicted values of the models and analytical results. The linear model shows perfect matching with the analytical outputs more than the other proposed mathematical formulations.

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“…Eqs. (35)(36)(37)(38)(39)(40)(41)(42)) are formulated using nonlinear regression analysis in SPSS statics 20 software for the given data in Table 4 as follows :…”

Section: Regression Modelsmentioning

confidence: 99%