With the increasingly serious global energy problem, clean energy sources such as solar energy have become the mainstream focus of development. Among these, solar Photovoltaic thermal (PVT) heat pump systems have prom...
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With the increasingly serious global energy problem, clean energy sources such as solar energy have become the mainstream focus of development. Among these, solar Photovoltaic thermal (PVT) heat pump systems have promising prospects. However, incorrect data transmitted by sensors can significantly impact the operation and control of the entire heat pump system, leading to reduced efficiency. Given the specific nature of PVT heat pump systems, their internal sensors are prone to errors during operation. To address these challenges, the Autoencoder Virtual in-situ calibration (AE-VIC) is applied to PVT heat pump systems. Preliminary studies have shown that this method can effectively reduce systematic and random errors in sensors. However, the current AE-VIC method faces certain issues, including unclear calibration targets and inefficient calibration of multiple sensors simultaneously, making it difficult to implement in practical systems. In order to overcome the limitations, faultdetection is integrated with AE-VIC. By combining the feature extraction capability of Autoencoder with a Softmax classifier, sensors with faults can be identified before the overall calibration process, making the calibration objective of the AE-VIC more targeted. Following faultdetection, inputs of the AE model are optimized using the mRMR algorithm for the identified faulty sensors. This optimization alleviates the difficulty of calibration. Through validation with actual system, the improved method effectively diagnoses and locates faulty sensors, and subsequently calibrates them. After calibration, the sensor system error can be reduced by over 95%. The improved calibration method surpasses the original AEVIC method in terms of both time and accuracy.
The rise of clean energy such as solar energy provides a new idea to optimize the energy structure, and Photovoltaic thermal (PVT) heat pump system is one of the mainstream development at present. The wrong sensor dat...
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The rise of clean energy such as solar energy provides a new idea to optimize the energy structure, and Photovoltaic thermal (PVT) heat pump system is one of the mainstream development at present. The wrong sensor data will have a great impact on the control and efficiency of the whole PVT heat pump system. In order to deal with this situation, Virtual in-situ calibration (VIC) based on Bayesian inference and Markov chain Monte Carlo (MCMC) is applied to PVT system. This paper presents a generic sensorfault diagnosis and calibration method for building energy systems using a PVT heat pump system as an example. Firstly, the data generated based on the mathematical model of PVT system is used to test the feasibility of VIC in this system. The results show that VIC can well reduce the systematic error and random error of the sensor. However, with the further study, it is found that in the actual system, it is impossible to establish a mathematical model which can reflect the relationship between the sensors with high accuracy. The VIC may fail or even have worse calibration results. Therefore, in order to solve the above problems, this study suggests using sparse autoencoder model instead of mathematical model. The model proved to be more accurate and to reflect the interconnectedness of the sensors, which helped the implementation of the VIC. The calibration method based on sparse autoencoder (SAE) can calibrate not only a single sensor in the PVT heat pump system, but also multiple sensors in a local area. After sparse autoencoder Virtual in-situ calibration (SAE-VIC) calibration, systematic and random errors of all sensors can be effectively reduced, and the accuracy of sensorcalibration can reach more than 90 %.
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