Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

Author:

Li Guannan1234,Wang Chongchong1,Liu Lamei1,Fang Xi5,Kuang Wei1,Xiong Chenglong1

Affiliation:

1. School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China

2. Anhui Province Key Laboratory of Intelligent Building and Building Energy-Saving, Anhui Jianzhu University, Hefei 230601, China

3. Key Laboratory of Low-Grade Energy Utilization Technologies and Systems (Chongqing University), Ministry of Education of China, Chongqing University, Chongqing 400044, China

4. State Key Laboratory of Green Building in Western China, Xi’an University of Architecture & Technology, Xi’an 710055, China

5. College of Civil Engineering, Hunan University, Changsha 410082, China

Abstract

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

Funder

Key Laboratory of Low-Grade Energy Utilization Technologies and Systems of the Ministry of Education of China

National Natural Science Foundation of China

State Key Laboratory of Green Building in Western China

Anhui Province Key Laboratory of Intelligent Building and Building Energy-Saving

“The 14th Five Year Plan” Hubei Provincial Advantaged Characteristic Disciplines (Groups) Project of Wuhan University of Science and Technology

Publisher

MDPI AG

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