HOME ENERGY CONSUMPTION ESTIMATION BY END USE AND ENERGY EFFICIENCY UPGRADE RECOMMENDATIONS

Abstract

Residential energy consumption is becoming an ever-growing sector, in 2012 accounting for 21.2% of total consumed energy in the United States. People have been focused on exploring potential strategies to lower the associated energy consumption, including energy efficiency improvement and policy adjustment. However, in the absence of detailed breakdown of energy consumption, it is hard to effectively apply energy efficiency upgrades, meaning a necessity of getting a deep understanding of the energy consumption estimates.

To achieve the breakdown of energy consumption, this project primarily focuses on the individual home energy consumption, through estimating the energy consumed in space heating, space cooling, water heating, and appliances. In particular, multiple-regression method is employed to construct the prediction model, and Residential Energy Consumption Survey (RECS) is used as the source data, which includes more than 12000 samples and has satisfactory data reliability.

As a result, many select factors are proven to be closely associated with certain energy end use. For instance, space heating/cooling energy consumption is greatly driven by the housing unit square feet, and a bigger house would result in more energy consumed in the heating and cooling sector, attested by a great coefficient in the constructed regression model. In addition, other geographic factors, such as climate region and heating degree-days, also indicate influences on the energy consumption pattern. This project further applied the regression models to establish a database of estimation by taking census data as input, providing reference cases for individual home energy efficiency upgrades.