Towards an understanding of campus-scale power consumption

@inproceedings{Bellala2011TowardsAU,
  title={Towards an understanding of campus-scale power consumption},
  author={Gowtham Bellala and Manish Marwah and Martin F. Arlitt and Geoff Lyon and Cullen Bash},
  booktitle={BuildSys@SenSys},
  year={2011}
}
Commercial buildings are significant consumers of electricity. In this paper, we collect and analyze six weeks of data from 39 power meters in three buildings of a campus of a large company. We use an unsupervised anomaly detection technique based on a low-dimensional embedding to identify power saving opportunities. Further, to better manage resources such as lighting and HVAC, we develop occupancy models based on readily available port-level network logs. We propose a semi-supervised approach… CONTINUE READING

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Key Quantitative Results

  • The experimental results over ten office cubicles show that the maximum error is less than 15% with an average error of 9.3%. We demonstrate that using our occupancy models, we can potentially reduce the lighting load on one floor (about 45 kW) by about 9.5%.

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