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 environmental control and data acquisition. This research aims determine whether these new models can estimate electrical energy consumption more accurately and whether it can be extended to provide forecasted information such as the temperature of the water at different regions within the EWH.
A custom data acquisition system is built to measure three-dimensional thermal variation and vertical thermal stratification in these tanks to have a better understanding of what is going on inside. From this, better data-driven and cost-effective models and strategies can be developed to be used for large-scale simulation and control of these tanks nationwide.
Read more about Daniel’s work on LinkedIn.