Recommended Citation
Postprint version. Published in Advances in Neural Networks, Lecture Notes in Computer Science, Volume 6064, January 1, 2010, pages 548-555.
The definitive version is available at https://doi.org/10.1007/978-3-642-13318-3_68.
Abstract
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 power usage history and weather conditions such as temperature, humidity, wind speed, etc. Different neural network configurations are discussed; satisfactory computer simulation results are obtained and presented.
Disciplines
Electrical and Computer Engineering
Copyright
2010 Springer.
URL: https://digitalcommons.calpoly.edu/eeng_fac/207