Model predictive control of space heating system with building integrated energy-efficient technologies - Wei, Zhichen (2024)
With the increasing energy prices and growing concerns over energy security, an accelerated transition to net zero carbon built environment has never been more important. The UK's building sector is a major contributor to greenhouse gas emissions, primarily due to heating energy, 83% of which is derived from natural gas combustion. To enhance energy efficiency and reduce GHGs, advanced control strategies targeting peak energy shifting are vital. Many studies have shown the capabilities of advanced control strategies such as model predictive control (MPC) to achieve energy efficiency, balance with thermal comfort and indoor air quality. It has also shown its capability to provide demand flexibility, minimising peak load demands and maximising the production of renewable energy sources in buildings. However, there remains a lack of research on the practicality of MPC for low-temperature heating systems, building-integrated energy-efficient systems, and assessing MPC under variable occupancies and future climates. Buildings with high occupancy variability, such as universities, where fluctuations occur throughout the day and across the year, can pose challenges in developing control strategies that aim to balance comfort and energy efficiency. While with changing climate conditions anticipated to modify building energy demands, especially in heating and cooling, the adaptability of MPC becomes vital. This study seeks to evaluate the effectiveness of MPC for low-temperature heating systems, integrated with energy-efficient technologies, considering the challenges posed by variable occupancy and projected climate conditions. The proposed approach integrated price responsive MPC with low temperature heating system and passive structural thermal energy storage (STES) and active storage tank. Integration of the system with photovoltaic (PV) system and solar hot water are also explored. The system performance under future climate conditions is evaluated considering different design and operation conditions, including different thermal masses, occupancy patterns and internal heat gains, setpoint strategies and operation temperatures of the low-temperature heating system. Moreover, recognising the absence of affordable and easily implemented solutions in the sector, we proposed a cost-effective MPC approach using internet of things (IoT) and dynamic pricing.