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.
Keywords
- Power prediction
- building energy management
- artificial neural network applications
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Wan, S., Yu, XH. (2010). Facility Power Usage Modeling and Short Term Prediction with Artificial Neural Networks. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_68
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DOI: https://doi.org/10.1007/978-3-642-13318-3_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
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