9th IFIP WG 12.6 and 1st IFIP WG 12.11 International Workshop on Artificial Intelligence for Knowledge Management, Energy, and Sustainability (AI4KMES) held at 30th International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Canada, 19 - 26 August 2021, vol.637, pp.44-62
The United States (US) is ranked as the second country with the highest carbon emission after China. The transformation to improve energy efficiency in the US has a critical global impact on carbon emission reduction. Therefore, any attempt towards transformation will count. Designing a new optimization model for the transformation of the kitchens is no exception and could be seen as innovative and realistic. This study aims to combine carbon emission reduction and energy efficiency by using a carbon tax system within the jurisdiction of local authorities to transform cooktop ovens in kitchens in the South Atlantic region. The South Atlantic census region is selected for the analysis due to its high propane usage. The carbon emissions are reduced by 1.2% with the proposed optimization model using the RECS (Residential Consumption Survey) data set. Based on the benefits of the first application, a new model is developed to look at the future with increasing demand. A regression-based machine learning is used in the R software to create a general model that predicts the efficiency increases. The model is constructed to assume that 100% of the propane cooktop ovens are converted into electric induction cooktop ovens. The proposed model will have two positive results. First, it will encourage the replacement of propane cooking devices with energy-efficient electric induction cooktop ovens to reduce carbon emissions. Second, the energy accessibility will be increased as energy-efficient appliances will be donated to the users by using the budget created through the carbon tax incomes.