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Whole building energy model (BEM) is difficult to beused in the classical model-based optimal control (MOC)because of its high-dimension nature and intensive computationalspeed. This study proposes a novel deep reinforcementlearning framework to use BEM for MOCof HVAC systems. A case study based on a real officebuilding in Pennsylvania is presented in this paper todemonstrate the workflow, including building modeling,model calibration and deep reinforcement learning training.The learned optimal control policy can potentiallyachieve 15% of heating energy saving by simply controllingthe heating system supply water temperature.
Citation: ASHRAE/IBPSA-USA Bldg Simulation Conf, Sept 2018
Product Details
- Published:
- 2018
- Number of Pages:
- 8
- Units of Measure:
- Dual
- File Size:
- 1 file , 470 KB
- Product Code(s):
- D-BSC18-C093
