Commercial buildings consume 19% of energy in the US as of 2010, and traditionally, their energy use has been optimized through improved equipment efficiency and retrofits. Beyond improved hardware and infrastructure, there exists a tremendous potential in reducing energy use through better monitoring and operation. We present several applications that we developed and deployed to support our thesis that building energy use can be reduced through sensing, monitoring and optimization software that modulates use of building subsystems including HVAC. We focus on HVAC systems as these constitute 48-55% of building energy use.
Specifically, in case of sensing, we describe an energy apportionment system that enables us to estimate real-time zonal HVAC power consumption by analyzing existing sensor information. With this energy breakdown, we can measure effectiveness of optimization solutions and identify inefficiencies. Central to energy efficiency improvement is determination of human occupancy in buildings. But this information is often unavailable or expensive to obtain using wide scale sensor deployment. We present our system that infers room level occupancy inexpensively by leveraging existing WiFi infrastructure. Occupancy information can be used not only to directly control HVAC but also to infer state of the building for predictive control.
Building energy use is strongly influenced by human behaviors, and timely feedback mechanisms can encourage energy saving behavior. Occupants interact with HVAC using thermostats which has shown to be inadequate for thermal comfort. Building managers are responsible for incorporating energy efficiency measures, but our interviews reveal that they struggle to maintain efficiency due to lack of analytical tools and contextual information. We present our software services that provide energy feedback to occupants and building managers, improves comfort with personalized control and identifies energy wasting faults.
For wide scale deployment of such energy saving software, they need to be portable across multiple buildings. However, buildings consist of heterogeneous equipment and use inconsistent naming schema, and developers need extensive domain knowledge to map sensor information to a standard format. To enable portability, we present an active learning algorithm that automates mapping building sensor metadata to a standard naming schema.
