Optimization of building energy usage presents an impactful and readily addressable industry opportunity. Commercial building operators have, over the past decade, invested in on-premise Building Management Systems (BMSs) to centrally monitor and operate building sensors and controllers. BMS configurations degrade over time due to changes in building occupancy patterns as well as from ongoing sensor and controller upgrades. Recent studies reveal that an additional 10% energy savings opportunity would be available if optimal BMS configurations were sustained.
Building operators face significant challenges in keeping BMS configurations optimized. The reasons are many. First, most BMSs offer proprietary interfaces that require custom, one-off integrations for remote access. Second, inconsistent BMS data representation makes it hard to aggregate and analyze performance data in order to operate systems with maximum efficiency. Third, BMSs are often designed as single user applications, creating complications to support multiple stakeholders that collectively dictate optimal usage.
We propose a hybrid cloud/on-premise model that addresses the limitations of current, on-premise BMS implementations and incorporates the benefits of new cloud technologies. Our hybrid model employs a cloud-based infrastructure “middle layer” (which we call GeoBMS) that connects the “top layer” of building performance applications with the “bottom layer” of existing brownfield BMS implementations. GeoBMS addresses BMS inaccessibility through virtualization; inconsistent data representation through common cloud data models; and lack of multi-stakeholder access through global authentication.
Through published APIs, GeoBMS enables the creation of innovative building performance applications. Applications use GeoBMS APIs to access previously unavailable on-premise BMS functionality and configuration data. We illustrate using a proof-of-concept application (which we call EnergyOptimize) that optimizes energy consumption for a museum case-example.