eprintid: 16320 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/63/20 datestamp: 2023-12-19 03:22:51 lastmod: 2023-12-19 03:22:51 status_changed: 2023-12-19 03:06:02 type: article metadata_visibility: show creators_name: Soomro, A.A. creators_name: Mokhtar, A.A. creators_name: Salilew, W.M. creators_name: Abdul Karim, Z.A. creators_name: Abbasi, A. creators_name: Lashari, N. creators_name: Jameel, S.M. title: Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data ispublished: pub keywords: Cooling; Digital storage; Forecasting; Heat storage; Learning systems; Mean square error; Median filters; Motion compensation; Nearest neighbor search; Stream flow; Support vector machines; Tanks (containers); Thermal energy, Cooling plants; District cooling; Hyper-parameter; K-near neighbor district colling; Performance; Prediction modelling; Support vectors machine; Thermal energy storage; Thermal energy storage tanks; Thermocline thickness, Neural networks, cluster analysis; machine learning; support vector machine, Cluster Analysis; Machine Learning; Neural Networks, Computer; Support Vector Machine note: cited By 5 abstract: 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. © 2022 by the authors. date: 2022 publisher: MDPI official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139812291&doi=10.3390%2fs22197687&partnerID=40&md5=216b8db38f61f11d662a35a68528e7c4 id_number: 10.3390/s22197687 full_text_status: none publication: Sensors volume: 22 number: 19 refereed: TRUE issn: 14248220 citation: Soomro, A.A. and Mokhtar, A.A. and Salilew, W.M. and Abdul Karim, Z.A. and Abbasi, A. and Lashari, N. and Jameel, S.M. (2022) Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data. Sensors, 22 (19). ISSN 14248220