Development of a Neural Network to Predict Residential Energy Consumption

Abstract

In Florida, an Energy Performance Index (EPI) calculation must be performed and submitted before a building permit can be granted. The EPI is a measure of energy efficiency calculated for all new construction and renovations. It uses data on several components of a structure to assign a rating (it must be under 100 points to pass). The lower the EPI, the more efficient the structure should be. This study involved the collection of residential EPI data from participating Gainesville Regional Utilities customers, matching this data with the actual energy consumption data and training an artificial neural network to relate the EPI to actual energy consumption. The ability of the artificial neural network to predict annual energy consumption will help residential designers/builders advance the goals of reducing the monthly cost of new housing and reducing the associated environmental impact and energy use. Data on new residential construction EPI calculations for 1998-2000 in Alachua County and corresponding energy consumption data for one year was compiled. This data was also matched with the conditioned living area of each house and then imported into the neural network. A local subdivision in which several houses were issued permits based on the same EPI was also identified as a control group so that normal variations in annual energy consumption could be determined. The neural network was used to create a model to predict annual energy consumption cost.