Methods and systems for training HVAC control using simulated and real experience data

US11525596B2 · kind B2 · utility

Assignee

• Johnson Controls Tyco IP Holdings LLP · US

Inventors

• Young-Min Lee · Seojong-myeon, KR
• Zhanhong Jiang · Milwaukee, US
• Viswanath Ramamurti · San Leandro, US
• Sugumar Murugesan · Santa Clara, US
• Kirk H. Drees · Cedarburg, US
• Michael J. Risbeck · Madison, US

Key dates

• Filing date: Dec 23, 2019
• Grant date: Dec 13, 2022
• Expiry date: Mar 28, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G05B13/04
• WIPO field: Control
• WIPO sector: Instruments

Abstract

Systems and methods for training a reinforcement learning (RL) model for HVAC control are disclosed herein. Simulated experience data for the HVAC system is generated or received. The simulated experience data is used to initially train the RL model for HVAC control. The HVAC system operates within a building using the RL model and generates real experience data. A determination may be made to retrain the RL model. The real experience data is used to retrain the RL model. In some embodiments, both the simulated and real experience data are used to retrain the RL model. Experience data may be sampled according to various sampling functions. The RL model may be retrained multiple times over time. The RL model may be retrained less frequently over time as more real experience data is used to train the RL model.