@ARTICLE{2017_3J_virtual,
  author={M. {Jin} and R. {Jia} and C. J. {Spanos}},
  journal={IEEE Transactions on Mobile Computing}, 
  title={Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence}, 
  year={2017},
  volume={16},
  number={11},
  pages={3264-3277},
  doi={10.1109/TMC.2017.2684806},
  abstract={Occupancy detection for buildings is crucial to improving energy efficiency, user comfort, and space utility. However, existing methods require dedicated system setup, continuous calibration, and frequent maintenance. With the instrumentation of electricity meters in millions of homes and offices, however, power measurement presents a unique opportunity for a non-intrusive and cost-effective way to detect occupant presence. This study develops solutions to the problems when no data or limited data is available for training, as motivated by difficulties in ground truth collection. Experimental evaluations on data from both residential and commercial buildings indicate that the proposed methods for binary occupancy detection are nearly as accurate as models learned with sufficient data, with accuracies of approximately 78 to 93 percent for residences and 90 percent for offices. This study shows that power usage contains valuable and sensitive user information, demonstrating a virtual occupancy sensing approach with minimal system calibration and setup.},
  url_pdf = {smart_meter_presence.pdf},
  url_supplementary = {smart_meter_presence_supp.pdf},
  url_link = {https://ieeexplore.ieee.org/document/7882676},
  url_media={https://spectrum.ieee.org/view-from-the-valley/energy/the-smarter-grid/what-does-your-smart-meter-know-about-you},
  keywords = "Smart city, Data mining, Machine learning",
  note={<a style="color:#FF0000" href="https://spectrum.ieee.org/view-from-the-valley/energy/the-smarter-grid/what-does-your-smart-meter-know-about-you">(Featured in 'IEEE Spectrum')</a>}
  }

{"_id":"sWmHBa2wBtFe3k3NT","bibbaseid":"jin-jia-spanos-virtualoccupancysensingusingsmartmeterstoindicateyourpresence-2017","author_short":["Jin, M.","Jia, R.","Spanos, C. J."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["M."],"propositions":[],"lastnames":["Jin"],"suffixes":[]},{"firstnames":["R."],"propositions":[],"lastnames":["Jia"],"suffixes":[]},{"firstnames":["C.","J."],"propositions":[],"lastnames":["Spanos"],"suffixes":[]}],"journal":"IEEE Transactions on Mobile Computing","title":"Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence","year":"2017","volume":"16","number":"11","pages":"3264-3277","doi":"10.1109/TMC.2017.2684806","abstract":"Occupancy detection for buildings is crucial to improving energy efficiency, user comfort, and space utility. However, existing methods require dedicated system setup, continuous calibration, and frequent maintenance. With the instrumentation of electricity meters in millions of homes and offices, however, power measurement presents a unique opportunity for a non-intrusive and cost-effective way to detect occupant presence. This study develops solutions to the problems when no data or limited data is available for training, as motivated by difficulties in ground truth collection. Experimental evaluations on data from both residential and commercial buildings indicate that the proposed methods for binary occupancy detection are nearly as accurate as models learned with sufficient data, with accuracies of approximately 78 to 93 percent for residences and 90 percent for offices. This study shows that power usage contains valuable and sensitive user information, demonstrating a virtual occupancy sensing approach with minimal system calibration and setup.","url_pdf":"smart_meter_presence.pdf","url_supplementary":"smart_meter_presence_supp.pdf","url_link":"https://ieeexplore.ieee.org/document/7882676","url_media":"https://spectrum.ieee.org/view-from-the-valley/energy/the-smarter-grid/what-does-your-smart-meter-know-about-you","keywords":"Smart city, Data mining, Machine learning","note":"<a style=\"color:#FF0000\" href=\"https://spectrum.ieee.org/view-from-the-valley/energy/the-smarter-grid/what-does-your-smart-meter-know-about-you\">(Featured in 'IEEE Spectrum')</a>","bibtex":"@ARTICLE{2017_3J_virtual,\n author={M. {Jin} and R. {Jia} and C. J. {Spanos}},\n journal={IEEE Transactions on Mobile Computing}, \n title={Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence}, \n year={2017},\n volume={16},\n number={11},\n pages={3264-3277},\n doi={10.1109/TMC.2017.2684806},\n abstract={Occupancy detection for buildings is crucial to improving energy efficiency, user comfort, and space utility. However, existing methods require dedicated system setup, continuous calibration, and frequent maintenance. With the instrumentation of electricity meters in millions of homes and offices, however, power measurement presents a unique opportunity for a non-intrusive and cost-effective way to detect occupant presence. This study develops solutions to the problems when no data or limited data is available for training, as motivated by difficulties in ground truth collection. Experimental evaluations on data from both residential and commercial buildings indicate that the proposed methods for binary occupancy detection are nearly as accurate as models learned with sufficient data, with accuracies of approximately 78 to 93 percent for residences and 90 percent for offices. This study shows that power usage contains valuable and sensitive user information, demonstrating a virtual occupancy sensing approach with minimal system calibration and setup.},\n url_pdf = {smart_meter_presence.pdf},\n url_supplementary = {smart_meter_presence_supp.pdf},\n url_link = {https://ieeexplore.ieee.org/document/7882676},\n url_media={https://spectrum.ieee.org/view-from-the-valley/energy/the-smarter-grid/what-does-your-smart-meter-know-about-you},\n keywords = \"Smart city, Data mining, Machine learning\",\n note={<a style=\"color:#FF0000\" href=\"https://spectrum.ieee.org/view-from-the-valley/energy/the-smarter-grid/what-does-your-smart-meter-know-about-you\">(Featured in 'IEEE Spectrum')</a>}\n }\n \n ","author_short":["Jin, M.","Jia, R.","Spanos, C. J."],"key":"2017_3J_virtual","id":"2017_3J_virtual","bibbaseid":"jin-jia-spanos-virtualoccupancysensingusingsmartmeterstoindicateyourpresence-2017","role":"author","urls":{" pdf":"http://www.jinming.tech/papers/smart_meter_presence.pdf"," supplementary":"http://www.jinming.tech/papers/smart_meter_presence_supp.pdf"," link":"https://ieeexplore.ieee.org/document/7882676"," media":"https://spectrum.ieee.org/view-from-the-valley/energy/the-smarter-grid/what-does-your-smart-meter-know-about-you"},"keyword":["Smart city","Data mining","Machine learning"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"http://www.jinming.tech/papers/myref.bib","dataSources":["sTzDHHaipTZWjp8oe","Y64tp2HnDCfXgLdc5"],"keywords":["smart city","data mining","machine learning"],"search_terms":["virtual","occupancy","sensing","using","smart","meters","indicate","presence","jin","jia","spanos"],"title":"Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence","year":2017}