Using historical data processing method to optimize Energy Hub

After the Energy Hub concept was proposed by Martin Geidl, it became a very popular topic for research into optimising the control and utilisation of energy resources. There are many widely-used methods to optimize Energy Hub operation; however many have limitations when the system is dynamic or the system behaviour is nonlinear. This paper provides a novel method to address those limitations. The method is based on an historical data processing technique to predict the Energy Hub load. By using this method, energy cost can be reduced by at least 12.3% per day.