Paper Conference

2018 Building Performance Analysis Conference and SimBuild co-organized by ASHRAE and IBPSA-USA

     

A Deep Reinforcement Learning Approach to Using Whole Building Energy Model for HVAC Optimal Control

Zhiang Zhang, Adrian Chong, Yuqi Pan, Chenlu Zhang, Siliang Lu, Khee Poh Lam
Carnegie Mellon University, Pittsburgh, PA
National University of Singapore, Singapore
Ghafari Associates, MI

Abstract: Whole building energy model (BEM) is difficult to be used in the classical model-based optimal control (MOC) because of its high-dimension nature and intensive computational speed. This study proposes a novel deep reinforcement learning framework to use BEM for MOC of HVAC systems. A case study based on a real office building in Pennsylvania is presented in this paper to demonstrate the workflow, including building modeling, model calibration and deep reinforcement learning training. The learned optimal control policy can potentially achieve 15% of heating energy saving by simply controlling the heating system supply water temperature.
Pages: 675 - 682
Paper:
simbuild2018_C093