ENERlite Consulting won the 2021 DOE’s Small Business Grant.
ENERlite Consulting, Inc. was awarded the SBIR/STTR 2021 Small Business grant from the Department of Energy for advancement of Building Energy Modeling. With this $200,000 grant, ENERlite Consulting, Inc. will develop a multistage calibration tool that integrates manual input with automated calibration to produce more relevant and realistic models of building energy use in this project, called “Automated Calibration Tool for Building Energy Models”.
Building energy modeling (BEM) is increasingly used in building industries for a variety of purposes, such as code compliance evaluation for new construction, continuous commissioning for existing buildings, and quantification of savings from energy efficiency projects. Building energy models without proper validation and calibration will lead to significant discrepancy between projected and actual building energy consumption. Deficiencies in predictive accuracy and consistency of BEM were identified as a barrier to increase BEM use. Supporting development and use of methods for model input calibration is identified as an initiative to address this problem.
The goal of the proposed project is to improve the predictive accuracy and consistency of building energy models by providing an educated and automated multi-stage calibration tool. Based on sensitivity analysis, meta-modeling, and gradient-based optimization, the proposed tool will identify the subset of model inputs that has the greatest influence on the model output and optimize this subset by minimizing the error between model output and monthly utility data.
Compared with the existing model calibration tools and processes, the proposed tool has a higher level of automation while enabling human-computer interaction for model input screening, measured data screening and post-calibration sanity check. This process will add physical insight and reduce the number of parameters and uncertainty bounds in the calibration process. The complexity reduction through meta-modeling in the proposed tool can also make the calibration process less computationally expensive.
