Predictions of Energy Consumption of Buildings' Life Cycle to Mitigate the Effects of Climate Change with a Focus on Energy Efficiency

Climate change and Energy are the prominent topics addressed by researchers in the 21st century. One of the concerns shown by the researchers is the amount of CO2 emitted by buildings across the globe. In 2020, the construction and demolition of buildings accounted for 37% of all energy-and process-related CO2emissions worldwide. There are methods to reduce the effect of buildings' construction and demolition on climate. Intelligent infrastructure could be one of the solutions for energy efficiency. Energy Usage Intensity, which measures how much energy a building uses annually per total gross floor area, must be known before implementing any solutions. Contribution of this proposed work is to predict Energy Usage Intensity for buildings of different states in different weather conditions in the United States while comparing three different statistical models of machine learning to find best results of prediction.