Lead Performer: Oak Ridge National Laboratory (ORNL) — Oak Ridge, TN
-- Molex LLC — Lisle, IL
-- PARC, A Xerox Company — Palo Alto, CA
DOE Total Funding: $1,000,058
Cost Share: $130,000
Project Term: October 1, 2016 – September 30, 2018
Funding Type: Building Energy Efficiency Frontiers and Innovations Technologies (BENEFIT) – 2016 (DE-FOA-0001383)

Project Objective

Advancing wireless sensor networks that are fully automated, plug-and-play, and capable of monitoring multiple parameters will enable a low-cost approach to accurately detect and diagnose faults, failures, and resulting inefficiencies in building equipment and subsystems, while also allowing for optimal localized whole-building control to improve occupant comfort and productivity while reducing energy use. This project will optimize the manufacturing process and improve the performance for a previously developed multi-parameter, self-powered, self-calibrating, peel-and-stick wireless sensor platform with a ≤$10/node cost target for scalable deployment in buildings. The project will advance the current state-of-the-art by a combination of component technology innovations and effective system-level integration with focus on the development of high-power density multi-modal energy harvesting and storage components to improve operational lifetime of the node, self-calibrating sensors using additive manufacturing and stochastic estimation techniques, and optimization of manufacturing methods to reduce cost while achieving functionality. The architecture developed will facilitate flexibility in incorporating different sets of sensing modalities and communication interfaces for a variety of building monitoring applications (e.g., damper fault detection and ambient temperature monitoring have different environmental operating conditions). System-level integration driven by interoperability requirements will be performed to optimize node- and network-level performance through standards-compliant communication interfaces with a variety of building automation systems. 


DOE Technology Manager: Marina Sofos
Lead Performer: Teja Kuruganti, Oak Ridge National Laboratory

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