Yearly District Cooling Company (DCC) needs to upgrade the plants to meet customer requirements. Moreover, this project will help the company to find when the demand load of flow will reach above the plant's installed capacity and how much increase is expected. So, the dataset was provided by the company for previous years. By applying ML and Arima, KNN, and SVM algorithms, it was found that the best result was the KNN algorithm with an accuracy of 94%, and the maximum flow load will be reached in summer of upcoming years. Hence, the company needs to take further action to upgrade the plant accordingly, either add a new chiller, modify the pumps, or any other suitable action.

Publication Date


Document Type

Master's Project

Student Type


Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research (Dubai)


Sanjay Modak

Advisor/Committee Member

Khalil Al Hussaeni


RIT Dubai