Title of Submission
Forecasting Residential Electricity Demand Through Machine Learning and Model Synthesis
Degree Program
Economics, MS
Major Advisor Name
Kelly Chen
Type of Submission
Scholarly Poster
Abstract
This paper aims to develop a predictive model of residential electricity demand using techniques from statistical science, data analysis and econometrics. Residential energy intensity is investigated as a critical component of demand and evaluated as a predictor of energy use per household using a panel data set compiled from the US Energy Information Administration. Statistical and machine learning methods are combined using an umbrella of linear regression, and predictive accuracy is tested for in-sample and out-of-sample validity.
COinS