Towards an understanding of campus-scale power consumption

  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},
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

Figures, Results, and Topics from this paper.

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%.


Publications citing this paper.


Publications referenced by this paper.

Similar Papers

Loading similar papers…