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Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of...

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Autores principales: Soomro, Afzal Ahmed, Mokhtar, Ainul Akmar, Salilew, Waleligne Molla, Abdul Karim, Zainal Ambri, Abbasi, Aijaz, Lashari, Najeebullah, Jameel, Syed Muslim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573049/
https://www.ncbi.nlm.nih.gov/pubmed/36236785
http://dx.doi.org/10.3390/s22197687
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author Soomro, Afzal Ahmed
Mokhtar, Ainul Akmar
Salilew, Waleligne Molla
Abdul Karim, Zainal Ambri
Abbasi, Aijaz
Lashari, Najeebullah
Jameel, Syed Muslim
author_facet Soomro, Afzal Ahmed
Mokhtar, Ainul Akmar
Salilew, Waleligne Molla
Abdul Karim, Zainal Ambri
Abbasi, Aijaz
Lashari, Najeebullah
Jameel, Syed Muslim
author_sort Soomro, Afzal Ahmed
collection PubMed
description In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others.
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spelling pubmed-95730492022-10-17 Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data Soomro, Afzal Ahmed Mokhtar, Ainul Akmar Salilew, Waleligne Molla Abdul Karim, Zainal Ambri Abbasi, Aijaz Lashari, Najeebullah Jameel, Syed Muslim Sensors (Basel) Article In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others. MDPI 2022-10-10 /pmc/articles/PMC9573049/ /pubmed/36236785 http://dx.doi.org/10.3390/s22197687 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Soomro, Afzal Ahmed
Mokhtar, Ainul Akmar
Salilew, Waleligne Molla
Abdul Karim, Zainal Ambri
Abbasi, Aijaz
Lashari, Najeebullah
Jameel, Syed Muslim
Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data
title Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data
title_full Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data
title_fullStr Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data
title_full_unstemmed Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data
title_short Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data
title_sort machine learning approach to predict the performance of a stratified thermal energy storage tank at a district cooling plant using sensor data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573049/
https://www.ncbi.nlm.nih.gov/pubmed/36236785
http://dx.doi.org/10.3390/s22197687
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