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A Hybrid Architecture for Building Energy Optimization

KARTHIK KRISHNAMURTHY, PRADEEP SINGH, NIKHIL SRIRAMAN

Abstract


Commercial buildings account for 15 percent of total energy consumption in the US alone. Owners and operators of large commercial properties have accordingly installed energy-saving devices and subsystems (such as sensors and controllers) within their building environments. They have also invested in building management software (BMS) to operate as master-controllers for these devices and subsystems. Unfortunately, current BMS implementations make it challenging to sustain optimal performance of these installed energy-saving devices and subsystems. In response, we propose a hybrid model that includes both cloud and on-premise components. This hybrid model: (i) virtualizes BMSs to overcome proprietary interfaces; (ii) offers uniform cloud data models to address inconsistent data representations; and (iii) manages global authentication to expand limited stakeholder access. The model employs an infrastructure “middle layer” (which we call GeoBMS) that connects the “top layer” of building performance applications with the “bottom layer” of existing BMS implementations. By reusing existing on-premise BMS implementations, our hybrid model protects existing investments and accelerates deployments. We offer a simple “proof of concept” application (which we call EnergyOptimizer) to validate GeoBMS’s capabilities using a museum floorplan as a case example.


DOI
10.12783/shm2019/32447

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