Data-Driven and Robust Predictive Control and Optimization: With Applications to Building Energy Management

Buildings, as major global energy consumers, can help mitigate the impact of growing renewable energy in smart grids through demand-side management (DSM). Smart energy management of buildings requires advanced control schemes that can cope with economic objectives, environmental uncertainties, occupant comfort, physical constraints, and external communication signals. Robust optimization (RO) and model predictive control (MPC) provide systematic and effective frameworks for this purpose. Beyond building energy management, RO and MPC methods are also fundamental to a broad range of engineering applications, such as chemical process planning, transportation, robotics, etc. This thesis focuses on RO and MPC design for linear systems as well as their applications in building energy management.  The research is organized into three topics:

• MPC designs for building energy management to enable energy-flexible DSM and improve environmental sustainability (Chapters 2–4).
• data-driven RO designs and algorithmic solutions for linear models to reduce conservatism and improve computational efficiency (Chapters 5 & 6).
• a distributionally robust MPC design for constrained linear systems to robustify control performance against additive model uncertainties and disturbances (Chapter 7).