Optimization models for energy- and EWH usage costs of schools and public buildings. Forecasting the internal temperature profile and energy profile of EWH units using control and machine learning techniques.
An opportunity is identified to use machine learning and control techniques to investigate the improvement of the two-node thermal model with the aid of a climatic chamber for environmental control and data acquisition. The project will determine whether these new models can estimate electrical energy consumption more accurately and whether it can be extended to provide other forecasted information such as the temperature of the water at different regions within the EWH. Firstly, a thermo-fluid model using FEM and CFD methods will be developed, followed by the further improvement of a climatic chamber to accommodate the data acquisition of more data points. The project investigates both horizontally- and vertically mounted EWHs.