  Demand response (DR) is becoming increasingly important as the volatility on
the grid continues to increase. Current DR approaches are completely manual and
rule-based or involve deriving first principles based models which are
extremely cost and time prohibitive to build. We consider the problem of
data-driven end-user DR for large buildings which involves predicting the
demand response baseline, evaluating fixed rule based DR strategies and
synthesizing DR control actions. We provide a model based control with
regression trees algorithm (mbCRT), which allows us to perform closed-loop
control for DR strategy synthesis for large commercial buildings. Our
data-driven control synthesis algorithm outperforms rule-based DR by $17\%$ for
a large DoE commercial reference building and leads to a curtailment of $380$kW
and over $\$45,000$ in savings. Our methods have been integrated into an open
source tool called DR-Advisor, which acts as a recommender system for the
building's facilities manager and provides suitable control actions to meet the
desired load curtailment while maintaining operations and maximizing the
economic reward. DR-Advisor achieves $92.8\%$ to $98.9\%$ prediction accuracy
for 8 buildings on Penn's campus. We compare DR-Advisor with other data driven
methods and rank $2^{nd}$ on ASHRAE's benchmarking data-set for energy
prediction.
