Data Driven Methods for Energy Reduction in Large Buildings

Modeling of HVAC components and energy flows for energy prediction purposes can be computationally expensive in large commercial buildings. More recently, the increased availability of building operational data has made it possible to develop data-driven methods for predicting and reducing energy use for these buildings. In this paper, we present such an approach, where we combine unsupervised and supervised learning algorithms to develop a robust method for energy reduction for large buildings operating under different environmental conditions. We compare our method against other energy prediction models that have been discussed in the literature using (1) a benchmark data set and (2) a real data set obtained from a building on the Vanderbilt University campus. A Stochastic Gradient Descent method is then applied to tune the controlled variable i.e., the AHU discharge temperature set point so that energy consumption is "minimized".