GeoBMS: Hybrid Cloud/ On-Premise Architecture for Building Energy Optimization

Building energy optimization offers an impactful and readily addressable opportunity for reducing energy consumption in commercial buildings. Owners/operators of commercial properties (building operators) have recognized this opportunity and invested significantly in both energy management devices (such as sensors and controllers) and systems (such as Building Management Systems, BMS) to be master-controllers for these installed devices. Recent studies have revealed that an additional 10% energy savings opportunity would become available if building operators can sustain optimal BMS configurations. Unfortunately, at least three significant challenges limit ongoing BMS configuration optimization. First, most BMSs have vendor-specific interfaces that require custom, one-off integrations for remote access. Second, a lack of consistency in how building configurations and performance data is represented from one BMS to another, makes it hard to aggregate performance trends. Third, BMSs often support single users, i.e., building operators, impeding the ability to support for multi-stakeholder applications such as for personalizing occupant experiences and planning disaster evacuations. We propose a hybrid cloud/on-premise model that reuses existing BMS implementations but incorporates the benefits of new cloud technologies. Our model employs a cloud-based “middle layer” infrastructure (which we call GeoBMS) that connects on-premise BMSs to scalable building management applications. The hybrid model addresses each of the identified BMS limitations: remote inaccessibility through virtualization; inconsistent data representation through unified cloud data models; and lack of multi-stakeholder access through global authentication. GeoBMS offers published APIs for applications to access virtualized infrastructure capabilities. We present a proof-of-concept application (which we call EnergyOptimize) that uses GeoBMS to monitor energy performance and optimize building configurations. We illustrate the application using a museum case-example.