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.

Thermal energy storage plays an important role in the energy management and has got great attention for many decades; stratification is a key parameter to be responsible for the performance of the stratified thermal energy storage tank. In this paper detailed study of modelling techniques used to analyse thermal energy storage has been conducted. The division of literature has been made as numerical, analytical, and artificial neural network-based. Numerical modelling being very physical based and required more specific software’s tools remain costly and computationally very complex at the same time it provides the detailed insights into the system, analytical model provide the exact solutions but need some assumptions which make the system unrealistic in some cases but is easy and flexible in terms of computational requirements, ANN though recently used modelling technique is a black box model which merely needs the data rather than any physical based complex calculations is attracting the scientific community.

In real-time streaming data, concept drift and class imbalance may occur simultaneously which causes the performance degradation of the online machine learning models. Most of the existing work is limited to addressing these issues for binary class data streams. Very little focus is given to the multi-class data streams. The most common approach to address these issues is ensemble learning. Ensemble learning consists of multiple classifiers combined which are trained on different subsets of the data to improve the overall accuracy. The performance of the ensemble learning approach suffers in case the new classifier is not trained on appropriate data (the data about the new concept). To address this gap, this study has proposed a Smart Adaptive Ensemble Model (SAEM) to address the issues of concept drift and class imbalance for multi-class data streams. The SAEM monitors the feature-level change in data distribution and creates a background ensemble to train the new classifier on features that observe change. To address the class imbalance issue, SAEM applies higher weights on the minority class instances using the dynamic class imbalance ratio. The proposed model outperformed the existing state-of-the-art approaches on the eight different data streams. The results showed an average improvement of 15.857% in accuracy, 20.35% in Kappa, 16.12% in F1-score, 15.58% in precision, and 16.42% in recall. The Friedman test confirmed statistically significant performance differences among all models across five key metrics. Based on the obtained results, the research findings strongly support the notion that SAEM exhibits enhanced effectiveness and efficiency as a solution for online learning applications.