Abstract
China’s “dual carbon” goals, energy conservation and emission reduction in the energy system, have become increasingly important. The sensor fault of an energy system will cause unstable operation and increase energy consumption. Therefore, this study proposes a new sensor fault detection strategy based on the data driven method for energy saving and emission reduction. However, for data-driven models, data quality has a greater impact on model performance. This study innovatively uses five machine learning methods to optimize the energy system operating data. Five machine learning methods include the moving average (MA), Lowess, Loess, Rlowess and Rloess methods. Fault detection performances of different data driven models optimized by different approaches are compared and analyzed. Besides, data outliers and parameter selection of data optimization methods are discussed. The results indicate that the MA method has the best optimization performance when the smoothness degree is level 2. The optimized data fluctuation range is controlled within the range of ±1. The fault detection accuracy rate of the model optimized based on the MA method is increased from 32.51% to 83.96% when the evaporation temperature sensor fault is a 5 °C deviation. However, the data will deviate from the original data trend when the smoothing parameter is set too large. Therefore, the smoothness of the data should not be too large. The approach proposed in this study is of great significance to the energy saving and emission reduction of the energy system.
Highlights
Received: 31 December 2021With the development of society, the energy consumption of buildings has been increasing year by year, which has accounted for nearly 40% of the world’s major energy needs [1]
This section first discusses the establishment of the fault detection model and analyzes the Q-statistic of the training data set for different data optimization methods
When the sensor fault detection models are optimized by five data smoothing methods, their Q-statistic fluctuations are alleviated
Summary
With the development of society, the energy consumption of buildings has been increasing year by year, which has accounted for nearly 40% of the world’s major energy needs [1]. Air conditioning and heating and ventilation systems account for nearly 50% of the total energy consumption of buildings [2,3]. With the launch of China’s “double carbon” policy, building energy saving and emission reduction will become more important [5,6,7]. The faults of the sensor will cause the system to run erratically, deviate from the normal operating mode, and increase the energy consumption [8,9,10]. Sensor fault detection is very meaningful for building energy saving and emission reduction and has received more and more attention
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
