Solar thermal process parameters forecasting for evacuated tube collectors (ETC) based on RNN-LSTM
Solar Heat for Industrial Process (SHIP) systems are a clean source of alternative and renewable energy for industrial processes. A typical SHIP system consists of a solar panel connected with a thermal storage system along with necessary piping. Predictive maintenance and condition monitoring of th...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
IIUM Press
2023
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/103059/7/103059_Solar%20thermal%20process%20parameters%20forecasting.pdf http://irep.iium.edu.my/103059/8/103059_Solar%20thermal%20process%20parameters%20forecasting_WOS.pdf http://irep.iium.edu.my/103059/ https://journals.iium.edu.my/ejournal/index.php/iiumej/article/download/2374/897/15722 https://doi.org/10.31436/iiumej.v24i1.2374 |
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| Summary: | Solar Heat for Industrial Process (SHIP) systems are a clean source of alternative and renewable energy for industrial processes. A typical SHIP system consists of a solar panel connected with a thermal storage system along with necessary piping. Predictive maintenance and condition monitoring of these SHIP systems are essential to prevent system downtime and ensure a steady supply of heated water for a particular industrial process. This paper proposes the use of recurrent neural network based predictive models to forecast solar thermal process parameters. Data of five
process parameters namely - Solar Irradiance, Solar Collector Inlet & Outlet Temperature, and Flux Calorimeter Readings at two points were collected throughout a four-month period. Two variants of RNN, including LSTM and Gated Recurrent Units, were explored and the performance for this forecasting task was compared. The results show that Root Mean Square Errors (RMSE) between the actual and predicted values
were 0.4346 (Solar Irradiance), 61.51 (Heat Meter 1), 23.85 (Heat Meter 2), Inlet Temperature (0.432) and Outlet Temperature (0.805) respectively. These results open up
possibilities for employing a deep learning based forecasting method in the application of SHIP systems. |
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