Online model estimation for predictive control of air conditioners in buildings.

Abstract

The foreseen deployment of advanced building automation technologies promises to turn passive consumers into active prosumers. Building automation technologies include communication, monitoring and control functionalities. These technologies can render great benefits to the power systems by increasing the flexibility of the demand side. Demand flexibility is widely acknowledged as a solution to increase the integration of the renewable energy sources, improve the operation of the transmission and distribution networks, and enhance the economic effectiveness of electricity markets. The aim of this Master thesis is to develop mathematical models to exploit the net load flexibility of corporate buildings with the objective of minimizing energy cost. This includes the development of simplified data-driven thermal models and decision aid tools to optimize the operation of distributed energy resources (e.g., HVAC) in buildings. The proposed tools will be developed within the framework of the European project GREsBAS and will be tested in a demo site (INESC TEC).