Impact of nonintrusive load monitoring on CO2 emissions in Malaysia

Abstract

Nonintrusive load monitoring (NILM) based energy efficiency can conserve electricity by creating awareness with the behaviour change and shrinking CO₂ emissions to the environment. However, the lack of effective models and strategies is problematic for policymakers to forecast quantitatively CO₂ emissions. This paper aims to study the impact of NILM on CO₂ emissions in Malaysia. Firstly, the predictive models were established based on Malaysia open data from 1996 to 2018. After that, scenario simulations were conducted to predict CO₂ emissions and NILM impact on environmental degradation in 2019-2030. The results revealed that a 12% reduction in electricity consumption due to NILM could contribute to a 10.2% shrinkage of the total CO₂ emissions. The result also statistically confirmed Malaysia to achieve a 45% reduction of CO₂ intensity in 2030. With NILM, the carbon reduction can be further enhanced to 60.2%. The outcomes provide valuable references and supporting evidence for policymakers in planning effective carbon emission control policies and energy efficiency measures. The work can be extended by developing a decision support system and user interfaces access via the cloud.