(formerly Secure Energy Algorithm Testbed)
-- National Renewable Energy Laboratory (NREL)—Golden, CO
-- Lawrence Berkeley National Laboratory—Berkeley, CA
-- City of San Francisco Department of Environment—San Francisco, CA
-- Digital Alchemy—Woodinville, WA
-- Recurve—Mill Valley, CA
Project Term: October 1, 2019 – Present (ongoing)
Funding Type: Lab Call
This project’s objective is to develop an Energy Data Vault (EDV), a secure means of storing energy consumption data and making it available to third parties while protecting individual data privacy. In concept, EDV will allow third parties to access public and protected data during run time, but outputs of the algorithms are validated to prevent protected or sensitive data to leave the testbed.
A variety of energy algorithms will be demonstrated using a mix of public and private data previously collected by the City of San Francisco and other stakeholders. Additionally, methods to generate synthetic energy data using energy models will be developed. This synthetic energy data will be useful for algorithm verification and prototyping.
A key use case is the development of automated measurement and verification algorithms (M&V 2.0), which estimate gross energy savings of installed efficiency projects using smart meter data, and have the potential to greatly reduce energy-efficiency program costs and allow for new pay-for-performance utility contracts. This project will implement the open-source savings calculations in a way that preserves value for energy market participants without disclosing protected individual data.
Over time, additional algorithms and tools will be incorporated into the Energy Data Vault that perform open-source calculations that can be validated and whose outputs can be shared under the same protections established for M&V outputs.
EDV has the potential to become a platform that supports an ecosystem of data and applications. By allowing algorithms to access protected data within the platform but restricting outputs in order to protect sensitive data, EDV will lower the barriers to participants in the growing M&V 2.0 industry. EDV will also make it easier to develop, test, and deploy new M&V 2.0 algorithms, further spurring innovation in the market.
Additional development of synthetic smart meter data, using OpenStudio models with multiple, staged interventions, faults, or program changes will allow for better characterization and validation of M&V 2.0 algorithms.
DOE project manager: Amir Roth
Principal Investigator: Janghyun Kim, NREL