SBIR Phase I: A Data-driven Demand Response Recommendation System

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to provide a data intelligence layer for buildings and electric grid operators, and help remove any guesswork from the implementation of optimized electricity demand response. Better demand response means that less efficient, and often more expensive, forms of electricity generation do not need to come online during times of high electricity demand. Demand response optimization reduces the stress on transmission and distribution systems, making them less likely to fail. A well implemented demand response program leads to competitive and efficient wholesale electricity market operations, and helps in keeping electricity prices in check. This Small Business Innovation Research (SBIR) Phase I project aims to develop, deploy, and evaluate innovative algorithms and software for control-oriented predictive modeling for energy systems, with specific application to electricity demand response. There are four significant technical hurdles that will be addressed by the proposed effort: (a) modeling complexity and heterogeneity, (b) limitations of rule-based demand response, (c) control complexity and scalability, and (d) interpretability of modeling and control. The goal of the proposed research is to address each of the technological hurdles, and in doing so develop (i) data-driven control oriented modeling tools for demand response and for large scale energy systems, and (ii) a methodology to preserve interpretability of our data-driven control-oriented models for providing energy analytics to the facilities manager based on real-world data. The proposed project will include pilot deployments on real buildings to evaluate the performance of the demand response software.