The presence or absence of occupants in a building has a direct effect on its energy use, as it influences the operation of various building energy systems. 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. This situation becomes even more complex when such buildings are integrated with renewable energy technologies, due to the inherently intermittent nature of these energy source. To promote widespread integration of renewable energy sources in such buildings, the adoption of advanced control strategies such as model predictive control (MPC) is imperative. However, the variable nature of occupancy patterns must be considered in its design. In response to this, the present study evaluates a price responsive MPC strategy for a solar thermal heating system integrated with thermal energy storage (TES) for buildings with high occupancy variability. The coupled system supplies the building heating through a low temperature underfloor heating system. A case study University building in Nottingham, UK was employed for evaluating the feasibility of the proposed heating system controlled by MPC strategy. The MPC controller aims to optimize the solar heating system’s operation by dynamically adjusting to forecasted weather, occupancy, and solar availability, balancing indoor comfort with energy efficiency. By effectively integrating with thermal energy storage, it maximizes solar energy utilization, reducing reliance on non-renewable sources and ultimately lowering energy costs. The developed model has undergone verification and validation process, utilizing both numerical simulations and experimental data. The result shows that the solar hot water system provided 63% heating energy in total for the case study classroom and saved more than half of the electricity cost compared with that of the original building heating system. The electricity cost saving has been confirmed resulting from the energy shifting from high price periods to medium to low price periods through both active and passive heating energy storages.

建筑物中是否有人居住对其能源使用有直接影响，因为它会影响各种建筑能源系统的运行。占用率变化较大的建筑物（例如大学）全天和全年都会出现波动，这可能会给制定旨在平衡舒适度和能源效率的控制策略带来挑战。当此类建筑与可再生能源技术集成时，由于这些能源固有的间歇性，这种情况变得更加复杂。为了促进可再生能源在此类建筑中的广泛整合，必须采用模型预测控制（MPC）等先进控制策略。然而，在其设计中必须考虑占用模式的可变性。对此，本研究评估了针对占用率变化较大的建筑物与热能存储 (TES) 集成的太阳能热采暖系统的价格响应 MPC 策略。耦合系统通过低温地暖系统为建筑物供暖。采用英国诺丁汉大学建筑案例研究来评估所提出的由 MPC 策略控制的供热系统的可行性。MPC 控制器旨在通过动态调整预测天气、占用率和太阳能可用性来优化太阳能供暖系统的运行，平衡室内舒适度与能源效率。通过与热能存储的有效结合，它可以最大限度地利用太阳能，减少对不可再生能源的依赖，最终降低能源成本。开发的模型已经利用数值模拟和实验数据进行了验证和验证。结果表明，太阳能热水系统为案例教室提供了总计63%的采暖能源，与原建筑采暖系统相比节省了一半以上的电费。通过主动和被动供热储能，将能源从高价时段转移到中低价时段，从而节省了电力成本。结果表明，太阳能热水系统为案例教室提供了总计63%的采暖能源，与原建筑采暖系统相比节省了一半以上的电费。通过主动和被动供热储能，将能源从高价时段转移到中低价时段，从而节省了电力成本。结果表明，太阳能热水系统为案例教室提供了总计63%的采暖能源，与原建筑采暖系统相比节省了一半以上的电费。通过主动和被动供热储能，将能源从高价时段转移到中低价时段，从而节省了电力成本。