Charging water load prediction with a multilayer perceptron for an efficient facility management and maintenance of thermal-energy-storage air-conditioning - Sanzana, Mirza Rayana (2024)
This research addresses the challenges in Thermal-Energy-Storage-Air-Conditioning (TES-AC) systems by developing a machine learning model for predicting the necessary water volume for chilling. TES-AC technology, utilizing thermal energy storage tanks, offers substantial benefits such as reduced chiller operation, cost savings, and lower carbon emissions. However, determining the optimal chilled water volume poses challenges. The primary objective is to design a machine learning model leveraging Multilayer Perceptron (MLP) for predicting water load, incorporating input variables like weather forecasts, day of the week, and occupancy data.