COMMUNITY ENERGY LABS INC — Department of Energy SBIR Phase II: C52-12b

Description

Electricity use drives the majority of greenhouse gas emissions from commercial buildings and is projected to increase 18% by 2040. Buildings in the Government, University and School sector in particular make up 28% of commercial space. This sector is ill-prepared to meet increasing complexity in state and local electricity rates and energy performance standards targeted toward efficiency, grid flexibility and self-consumption of on-site renewables. Neglecting this substantial and underserved market segment jeopardizes the DOE’s goal to achieve an equitable transition to a decarbonized energy system by 2050. Model Predictive Control has been shown to be highly effective at reducing greenhouse gas emissions while also saving money and meeting occupant comfort; however, model data collection and setup for building Model Predictive Controllers is costly, time consuming and often inaccurate. Phase I of this project combined multiple novel alternative data collection methods in Easy Does ItTM (EDI) and found that EDI could reduce Model Predictive Control data collection time by 17-38% (5-32 hours). EDI has the potential to reduce customer onboarding cost by 50% without impacting the accuracy of the resulting Model Predictive Controller. In this Small Business Innovation Research Phase II proposal, the applicant company proposes to carry out full product development and field testing of the EDI software application in order to autonomously translate user-friendly prompts and digital information into model parameter inputs for setup and calibration of two open source hybrid gray-box Model Predictive Control frameworks using primary and secondary school building reference models as a starting point. This project enables accurate and affordable Model Predictive Control integration into building automation software - providing a pathway for leanly staffed small and mid-sized commercial building customers to gain control of electricity bills and carbon emissions. The applicant company will bring to market a scalable, autonomous, clean building control system of hardware, software and cloud services that decreases the installed cost and complexity of Model Predictive Control for a solution serving small to mid-sized commercial building owners. This will increase energy savings by 5-25%, reduce peak demand by 20-50% and drive adoption of renewables through load shifting. Savings can be invested into resources for learning, community services, or, improving occupants’ health and safety.