A high-fidelity energy monitoring and feedback architecture for reducing electrical consumption in buildings

Existing solutions in commercial building energy monitoring are insufficient in identifying energy waste or for guiding improvement. This is because they only provide usage statistics in aggregate, both spatially and temporally. To significantly and sustainably reduce energy usage in buildings, we need an architecture and a system implementation that provide high-fidelity real-time visibility into each component of the building. We propose a three-tiered architecture consisting of sensing, data delivery and representation, and applications and services . We show that this layering allows us to cleanly abstract the low-level details of the myriads of disparate monitoring instruments and protocols, provide an uniform data representation interface, and enable innovation in portable building applications. This thesis further explores each layer in detail and present design decisions and findings. Building on top of this architecture, we propose an application process flow for energy data analysis and visualization, substantiated by a real deployment. This process consists of three parts: first, to understand and instrument the load tree; second, to conduct data analysis, modeling, and disaggregation of energy usage statistics; and third, combined with meta-data, to re-aggregate individual load usages into actionable representations for visualization and feedback to the occupants. Finally, we evaluate the proposed architecture and process flow with a diverse class of building applications, visualizations, and deployments.