A fast calibration algorithm for Non-Dispersive Infrared single channel carbon dioxide sensor based on deep learning

Mao, Keji; Xu, Jing; Jin, Runhui; Wang, Yuxiang; Fang, Kai

doi:10.1016/j.comcom.2021.08.003

Cited by 16 publications

(3 citation statements)

References 22 publications

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“…Taking the measurement uncertainty estimated by orthogonal regressions as an indicator, the latter methodology shows better agreement between the values recorded by the sensors and the reference measurements [ 18 ]. Mao et al [ 19 ] proposed a deep-learning-based algorithm for rapid calibration of carbon dioxide sensors characterised by high efficiency, accuracy and low cost. As a main result, a back-propagation neural network was chosen as the model.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Assessment of a Set of CO2 Concentration Sensors in an In-Use Office Building

Lapuente

Jiménez

Sánchez

2022

Sensors

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The measurement of the CO2 concentration has a wide range of applications. Traditionally, it has been used to assess air quality, with other applications linked to the experimental assessment of occupancy patterns and air renewal rates. More recently, the worldwide dissemination of COVID-19 establishing a relationship between infection risk and the mean CO2 level has abruptly led to the measurement of the CO2 concentration in order to limit the spread of this respiratory disease in the indoor environment. Therefore, the extensive application of this measurement outside of traditional air quality assessment requires an in-depth analysis of the suitability of these sensors for such modern applications. This paper discusses the performance of an array of commercial wall-mounted CO2 sensors, focusing on their application to obtain occupancy patterns and air renovation rates. This study is supported by several long-term test campaigns conducted in an in-use office building located in south-eastern Spain. The results show a spread of 19–101 ppm, with a drift of 28 ppm over 5 years, an offset of 2–301 ppm and fluctuations up to 80 ppm in instantaneous measurements not related to concentration changes. It is proposed that values averaged over 30 min, using a suitable reference value, be used to avoid erroneous results when calibration is not feasible.

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Section: Related Workmentioning

confidence: 99%

Long-Term Assessment of a Set of CO2 Concentration Sensors in an In-Use Office Building

Lapuente

Jiménez

Sánchez

2022

Sensors

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“…In the domain of wireless sensor networks (WSNs), a noticeable trend is the integration of artificial intelligence (AI) and machine-learning (ML) models for processing and interpreting the steady influx of time-series data [ 8 , 9 , 10 , 11 ]. The development and application of these models often necessitate a significant volume of data points, ranging from thousands [ 12 ] to hundreds of thousands [ 13 ]. It is worth noting that non-eventful data holds almost equal importance to eventful data for the training and validation of these models.…”

Section: Introductionmentioning

confidence: 99%

Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks

Fay,

Corcoran,

Diamond

2023

Sensors

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This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO2 emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8–99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.

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“…In thermoelectric load forecasting, classical methods include regression analysis (Qing et al, 2013), time series methods, mathematical and statistical methods such as Kalman filtering (Dong et al, 2015). Machine learning was gradually introduced into short-term load forecasting (Greff et al, 2016;Geysen et al, 2018), such as expert systems (Chen et al, 1991), fuzzy forecasting (Jović, 2021), wavelet analysis (Kumbinarasaiah et al, 2023), chaos theory (Al-Shammari et al, 2016), support vector machines (Kuzishchin and Ismatkhodzhaev, 2020;Razzak et al, 2020), cluster analysis models (Liu et al, 2020) and artificial neural networks (Mao et al, 2021;Wang et al, 2022a;Wang et al, 2022b;Yang et al, 2022).…”

mentioning

confidence: 99%

Industrial steam consumption analysis and prediction based on multi-source sensing data for sustainable energy development

Yang

Xu²,

Cheng³

et al. 2023

Front. Environ. Sci.

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Centralized heating is an energy-saving and environmentally friendly way that is strongly promoted by the state. It can improve energy utilization and reduce carbon emissions. However, Centralized heating depends on accurate heat demand forecasting. On the one hand, it is impossible to save energy if over producing, while on the other hand, it is impossible to meet the heat demand of enterprises if there is not enough capacity. Therefore, it is necessary to forecast the future trend of heat consumption, so as to provide a reliable basis for enterprises to reasonably deploy fuel stocks and boiler power. At the same time, it is also necessary to analyze and monitor the steam consumption of enterprises for abnormalities in order to monitor pipeline leakage and enterprise gas theft. Due to the nonlinear characteristics of heat load, it is difficult for traditional forecasting methods to capture data trend. Therefore, it is necessary to study the characteristics of heat loads and explore suitable heat load prediction models. In this paper, industrial steam consumption of a paper manufacturer is used as an example, and steam consumption data are periodically analyzed to study its time series characteristics; then steam consumption prediction models are established based on ARIMA model and LSTM neural network, respectively. The prediction work was carried out in minutes and hours, respectively. The experimental results show that the LSTM neural network has greater advantages in this steam consumption load prediction and can meet the needs of heat load prediction.

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A fast calibration algorithm for Non-Dispersive Infrared single channel carbon dioxide sensor based on deep learning

Cited by 16 publications

References 22 publications

Long-Term Assessment of a Set of CO2 Concentration Sensors in an In-Use Office Building

Long-Term Assessment of a Set of CO2 Concentration Sensors in an In-Use Office Building

Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks

Industrial steam consumption analysis and prediction based on multi-source sensing data for sustainable energy development

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