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Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load

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  • Ntumba Marc-Alain Mutombo

    (Department of Electrical Engineering, Mangosuthu University of Technology, Umlazi 4031, South Africa)

  • Bubele Papy Numbi

    (Department of Electrical Engineering, Mangosuthu University of Technology, Umlazi 4031, South Africa)

Abstract

Energy consumption in the building sector is a major concern, particularly in this time of worldwide population and energy demand increases. To reduce energy consumption due to HVAC systems in the building sector, different models based on measured data have been developed to estimate the cooling load. The purpose of this work is to develop a linear regression model for cooling load of a research room based on the radiant time series (RTS) components of the cooling load that consider the building material and the environment. Using the forward step method, linear regression models were developed for both all-seasons and seasonal data from three years of cooling load data obtained from the RTS method for a research room at Mangosuthu University of Technology (MUT), South Africa. The male and female occupants, window cooling load, and roof cooling load were found to be the most influential predictors for the cooling load model. The obtained relative errors between the best all-seasons model and seasonal models built with the same predictors for the respective data subsets are almost zero and are given as 0.0073% (autumn), 0.0016% (spring), 0.0168% (summer), and 0.0162% (winter). This leads to the conclusion that the seasonal models can be represented by the all-seasons model. However, further study can be performed to improve the model by incorporating the occupancy behaviours and other components or parameters intervening in the calculation of cooling load using the radiant time series method.

Suggested Citation

  • Ntumba Marc-Alain Mutombo & Bubele Papy Numbi, 2022. "Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load," Energies, MDPI, vol. 15(14), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5097-:d:861347
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