TY  - JOUR
N2  - 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.
N1  - cited By 5
IS  - 19
KW  - 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
KW  -  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
KW  -  Neural networks
KW  -  cluster analysis; machine learning; support vector machine
KW  -  Cluster Analysis; Machine Learning; Neural Networks
KW  -  Computer; Support Vector Machine
TI  - Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data
ID  - scholars16320
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139812291&doi=10.3390%2fs22197687&partnerID=40&md5=216b8db38f61f11d662a35a68528e7c4
JF  - Sensors
A1  - Soomro, A.A.
A1  - Mokhtar, A.A.
A1  - Salilew, W.M.
A1  - Abdul Karim, Z.A.
A1  - Abbasi, A.
A1  - Lashari, N.
A1  - Jameel, S.M.
VL  - 22
Y1  - 2022///
SN  - 14248220
PB  - MDPI
ER  -