Facility Power Usage Prediction with Artificial Neural Networks

Residential and commercial buildings accounted for about 68% of the total U.S. electricity consumption in 2002. Improving the energy efficiency of buildings can save energy, reduce cost, and protect the global environment. In this research, artificial neural network is employed to model and predict the facility power usage of campus buildings. The prediction is based on the building and the weather conditions such as temperature, humidity, wind speed, etc. Various neural network configurations are discussed; satisfactory computer simulation results are obtained and presented.

Name thesis:degree_name

MS in Electrical Engineering

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2009

Author dc:creator

• Wan, Sunny

Contributors dc:contributor

• Helen Yu

Identifier

10.15368/theses.2009.115

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-1148