Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions

Rafael Pino-Mejías, Alexis Pérez-Fargallo, Carlos Rubio-Bellido and Jesús A. Pulido-Arcas

Energy, 2017, vol. 118, issue C, 24-36

Abstract: An attempt has been made to develop linear regression models and Artificial Neural Networks (ANN) to predict the heating and cooling energy demands, energy consumptions and CO2 emissions of office buildings in Chile. The calculation of dependent variables to calibrate and evaluate the models has been determined starting from the ISO 13790:2008 standard, assigning constructive characteristics to each of the geometries studied based on the Chilean standards, studying 77,000 cases. A total of 8 fundamental variables have been considered to cover the design parameters. In energy consumption and CO2 emissions cases, the linear regression models that offer a better performance are those where the predictive variables have been transformed. Whereas, the multilayer perceptron adjusted over the variables without being transformed, provides greater accuracy in the determination of the demand, consumption and CO2 emissions both for heating and cooling, offering ECM values closer to 0, with an R2 coefficient above 99%. It is foreseen that the models developed can be used to estimate the energy saving between different design outlines during the project phases when the construction standards, systems and internal loads are defined.

Keywords: Neural networks; Energy demand; Energy consumption; CO2 emissions; Energy efficiency (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:118:y:2017:i:c:p:24-36

DOI: 10.1016/j.energy.2016.12.022

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