Model predictive control of space heating system with building integrated energy-efficient technologies

Wei, Zhichen (2024) Model predictive control of space heating system with building integrated energy-efficient technologies. PhD thesis, University of Nottingham.

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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.

The developed coupled model has undergone rigorous verification and validation using both numerical simulations and experimental data, demonstrating excellent agreement between the model predictions and observed outcomes. The study's findings indicate that the implementation of mediumweight thermal mass and a medium-temperature (45℃) under-floor heating inlet temperature enhances load shifting capabilities, considering a realistic occupancy profile and a high tolerance setpoint strategy during unoccupied periods.

The research also confirms the feasibility of implementing MPC to regulate phase change material (PCM) wallboard integrated into the building envelope, leading to electricity cost savings of up to 35%. Moreover, the introduction of rooftop photovoltaic (PV) electrical energy into the building energy supply network further improved energy shifting potential. Employing a solar hot water system for energy shifting resulted in more than half of the thermal energy cost savings compared to the original heating system. The result also showed that higher low-price energy usage and a lower heating energy usage could be achieved in future climate conditions. Finally, the experimental evaluation of the MPC showed that the proposed approach result in a 24% electricity cost reduction as compared to a conventional control strategy. This research provides significant insights into intelligent localised MPC control development for the built environment, offering a diverse array of energy-efficient technologies for building integration.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Calautit, John
Wu, Yupeng
Keywords: Model predictive control; Low-temperature heating systems; Variable occupancy
Subjects: T Technology > TH Building construction > TH7005 Heating and ventilation. Air conditioning
Faculties/Schools: UK Campuses > Faculty of Engineering > Built Environment
Item ID: 77403
Depositing User: wei, zhichen
Date Deposited: 18 Jun 2024 13:39
Last Modified: 18 Jun 2024 13:39

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