Abstract

Energy-saving strategies are required to address the increasing global CO2 and electrical energy consumption problems. Therefore, the determinant factors of electrical energy consumption consist of socio-demographic changes, occupant behavior, house and appliance characteristics, or so-called techno-socioeconomic factors, which all need to be assessed. Statistics models, such as the artificial neural network (ANN), can investigate the relationship among those factors. However, the previous ANN model only used limited factors and was conducted in the developed countries of subtropical regions with different determinant factors than those in the developing countries of tropical regions. Furthermore, the previous studies did not investigate the various impacts of techno-socioeconomic factors concerning the performance of the ANN model in estimating monthly electrical energy consumption. The current study develops a model with a more-in depth architecture by examining the effect of additional factors such as socio-demographics, house characteristics, occupant behavior, and appliance characteristics that have not been investigated concerning the model performance. Thus, a questionnaire survey was conducted from November 2017 to January 2018 with 214 university students. The best combination factors in explaining the monthly electrical energy consumption were developed from occupant behavior, with 81% of the variance and a mean absolute percentage error (MAPE) of 20.6%, which can be classified as a reasonably accurate model. The current study’s findings could be used as additional information for occupants or for companies who want to install photovoltaic or wind energy systems.

Highlights

  • The results of the artificial neural network (ANN) model based on single factors showed the following:

  • The model based on house characteristics by Broyden–Fletcher–Goldfarb–Shanno explained 49% of the variance with a mean absolute percentage error (MAPE) of 22.5%

  • The model based on socio-demographic and appliance characteristics factors using the one-step secant showed the best performance with a MAPE of 21% and an R2 of 0.59

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Summary

Introduction

The increasing CO2 level worldwide, due to electrical energy consumption, is the fundamental factor of environmental damage such as the depletion of the ozone layer, global warming, and climate change. The emission of CO2 has increased at a rate of 1.3%. The emission of CO2 can be classified as being from sources such as coal, natural gas, and oil in the industry and building sectors. The building sector consists of commercial and residential sectors that contribute 28% of the global energy-related CO2 emissions [2]. Malaysia had a global commitment to reduce 45% CO2 emission intensity by 2030, as stated in the Kuala Lumpur Low Carbon Society 2030 Blueprint [3].

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