Towards a Scalable Model for Smart Buildings
Smart, internet-connected buildings present great opportunities to scale operational energy savings. Progress is evident, with several software companies introducing new products over the last decade offering abilities to access, store, visualize, and analyze utility meter and building automation system data. Yet the rate of growth in advanced optimization has been slower in buildings applications than observed in other areas. The authors argue that efforts to deploy smart analytics in buildings are substantially hindered by four barriers specific to the sector: proprietary data architectures and communication protocols, low-performing legacy hardware, lack of contextual information (metadata) for data, and poor data quality. Based on deployment experience in a campus building portfolio of 1.6 million square feet, the authors present approaches to overcome these four barriers in order to increase the scale of potential energy savings and facilitate widespread adoption of smart building capabilities.