@proceedings{34233,
  author = {Marco Pritoni and Chris Weyandt and Diedre Carter and John Elliott},
  title = {Towards a Scalable Model for Smart Buildings},
  abstract = {<p><span>Smart, internet-connected build</span><span>ings present great opportunities</span><span> to scale operational </span><span>energy savings. Progress is evide</span><span>nt, with several software comp</span><span>anies introducing new products </span><span>over the last decade offering abilities to access, store, visua</span><span>lize, and analyze utility meter and </span><span>building automation system data. Y</span><span>et the rate of growth in adva</span><span>nced optimization has been </span><span>slower in buildings applications</span><span> than observed in other areas. </span><span>The authors argue that efforts to </span><span>deploy smart analytics in buildi</span><span>ngs are substantially hindered </span><span>by four barriers specific to the </span><span>sector: proprietary data archi</span><span>tectures and co</span><span>mmunication protoc</span><span>ols, low-performing legacy </span><span>hardware, lack of contextual inf</span><span>ormation (metadata) for data, a</span><span>nd poor data quality. Based on </span><span>deployment experience in a campus building portfolio of 1.6 mil</span><span>lion square feet, the authors </span><span>present approaches to overcome t</span><span>hese four barriers in order to </span><span>increase the scale of potential </span><span>energy savings and facilitate wi</span><span>despread adoption of smart buil</span><span>ding capabilities.</span></p>},
  year = {2018},
  journal = {ACEEE Summer Study on Energy Efficiency in Buildings},
  month = {06/2020},
  url = {https://escholarship.org/uc/item/5b7966hh#author},
  language = {eng},
}