Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a black-box virtual sensing framework for accurate indoor temperature monitoring. It automatically selects optimal sensors and develops a predictive model, reducing costs and improving efficiency in diverse environments.
Area Of Science
- Environmental monitoring
- Sensor networks
- Artificial intelligence
Background
- Real-time indoor temperature monitoring is crucial for comfort, legal compliance, and energy efficiency.
- Deploying adequate physical sensors for uniform temperature monitoring can be challenging and costly.
- Virtual sensing offers a solution by replacing physical sensors with virtual ones, enabling monitoring of inaccessible areas and reducing deployment expenses.
Purpose Of The Study
- To develop a versatile, black-box virtual sensing framework for indoor temperature monitoring, independent of specific environmental characteristics.
- To systematically analyze and develop novel distance metrics for optimal sensor selection.
- To create an automated approach for identifying the most effective subset of sensors and predicting temperatures.
Main Methods
- Systematic analysis of various distance metrics for sensor selection.
- Genetic programming to design a novel, combined distance metric.
- Development of a general, automated approach for subset sensor selection.
- Assessment of different prediction strategies using selected sensors and evaluation of output reliability.
Main Results
- A novel distance metric was developed using genetic programming, outperforming individual distance metrics.
- An automated method for selecting the optimal subset of sensors was proposed.
- The proposed black-box virtual sensing framework demonstrated accurate and efficient temperature monitoring in an open office scenario.
- The framework successfully derived a virtual sensing model capable of predicting temperatures based on selected sensor data.
Conclusions
- The developed black-box virtual sensing framework is effective for accurate and efficient indoor temperature monitoring.
- The automated sensor selection and virtual sensing model generation can be adapted to various indoor environments.
- This approach offers a cost-effective and reliable solution for temperature monitoring challenges.
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