Building energy systems account for about 30% of global energy consumption and play a crucial role in energy transition and carbon neutrality. Residential buildings contribute a significant share of building energy consumption. An energy storage system equipped with domestic hot water tank and electrochemical battery is one of the most potential type of future residential building energy storage systems, which could offer substantial flexibility for renewable energy integration in residential buildings. However, the inherent complexity and stochastic nature of residential building energy storage systems require predictive control to enable effective demand response. Although Model Predictive Control (MPC) is widely applied in building energy systems, its performance is challenged by model and load uncertainties as well as limited controller transferability. In practice, data scarcity and constrained computational resources further complicate deployment. Existing approaches, such as hierarchical MPC, robust MPC, and learning-based MPC, often suffer from suboptimality or inadequate data resources. To address these challenges, this study proposes an adaptive model predictive control framework with an online-updated model based on real-time operational data. By combining an online-updated heat pump model with model order reduction and an adaptive probabilistic error correction scheme, the proposed method reduces computational complexity while preserving accuracy and enhancing controller generalizability. Case studies demonstrate that AMPC achieves a 15.2% cost reduction compared to rule-based control and a 12.1% saving relative to time-varying MPC. Under uncertain conditions, it improves comfort levels by 80%. And its validated transferability across different systems highlights its potential for large-scale deployment.

The current standards for evaluating chiller-plant energy performance provide recommended energy-efficiency values only for annual or rated conditions. The continuous commissioning of chiller plants, based on real-world operating conditions, is crucial and can increase their energy efficiency; however, the existing commissioning methods are too general to provide practical steps for system retrofitting and do not grade the retrofitting steps in terms of economy and difficulty. To address this, this study proposes a stepwise commissioning method to predict the energy efficiency potential of a chiller plant from operational parameters measured using basic sensors. In this method, improvements in efficiency via retrofitting are estimated based on the optimization of the control strategy, equipment performance, and system structure. Based on this method, for the high-performance chiller plant examined as a case study, control-strategy optimization could improve the system energy efficiency ratio from 5.2 to 5.7, an 8.7 % increase; optimizing equipment performance could further improve it from 5.7 to 5.9, a 4.6 % increase. In terms of different types of equipment, replacing the chillers, water pumps, and cooling towers would achieve improvements in energy efficiency, of 2.7 %, 0.3 %, and 1.5 %, respectively, as the existing equipment is already efficiently operated. This method therefore supports improvements in chiller-plant energy efficiency considering economic feasibility and implementation difficulty.

The constant temperature and humidity air-conditioning systems extensively applied in buildings and spaces, such as clean rooms and manufacturing facilities, are considerably energy intensive. To provide thermal comfort, ensure working efficiency, or maintain positive pressures in a workshop, a large amount of fresh air must be supplied indoors. The reasonable use of fresh air can reduce the cost (such as energy, CO2 emission, or money cost) of the air-handling process. An optimal fresh-air utilization strategy was developed to minimize the cost of operation based on considerations of the different costs for different handling processes in all working conditions. Straightforward isocost lines were developed to determine the optimal fresh-air ratio. Using this method, the performances of heating, ventilation, and air-conditioning systems in a workshop were simulated and tested. Results demonstrate that the proposed optimal strategy exhibits superior performance in terms of cost savings. Compared with commonly used strategies, the optimal strategy can reduce the total annual primary energy consumption by 8.3%–9.7%; and the reduction can be as high as 35% in the transition seasons. When the optimal fresh-air utilization strategy is applied in the field, the primary energy consumption is reduced by 6.4%–9.8% on typical days.

In the smart grid, residential inverter air conditioners (AC) with significant demand response (DR) potential due to their load flexibility and as the major contributors to peak electricity, need to be grid-responsive to relieve power supply-demand imbalance and ensure thermal comfort. Model predictive control (MPC) has strong capabilities for unlocking the flexibility of residential buildings to realize DR by responding to electricity prices. However, the high computational requirements and complex control system integration processes make the application of MPC a significant challenge. A hierarchical nonlinear MPC (HNLMPC) is developed to realize grid-responsive control for residential inverter ACs by responding to real-time electricity price signals. The controller consists of three parts: the upper-level supervisor MPC, the lower-level optimal PID controller, and the signal converter. The indoor air temperature is selected as the optimized setpoint sequence passed from the upper level to the lower level. A nonlinear prediction model is developed considering the dynamic performances of the inverter AC and the coupled thermal response of an air-conditioned room. A test platform is constructed using Simulink and Simscape to access the DR performance of HNLMPC by comparing it with different rule-based control methods, hierarchical linear MPC, and centralized MPC. The control results show that HNLMPC can achieve peak load shifting and peak shaving without sacrificing thermal comfort by adjusting the room temperature to charge and discharge cooling for the building's thermal mass. Additionally, it enables plug-and-play capability for practical applications, reducing the dependency on local computing power and the need for accurate models. Compared to basic rule-based control, HNLMPC reduces peak-hour energy consumption by 31.6% and total electricity costs by 14.3% over the entire cooling season.

Full electrification of building energy systems makes that the electricity-driven heat pump and solar heat become the most promising heating sources for hot water production in the future. The heat pump assisted solar water heater will be a good solution to jointly use these two heating sources. The system has large potential in demand response because of large capacity thermal storage tank which requires reasonable optimal control. In this context, this paper presents an adaptive model predictive control (AMPC) to achieve demand response with adaptive boundary and linear time-varying heat pump efficiency. Using adaptive parameters, the controller improves the adaptability and achieves optimal control under various disturbance conditions. The results demonstrated the AMPC realize 20% cost saving and 12% energy saving compared with PID, which are 7% reduction in costs and a 3% decrease in energy consumption compared to conventional MPC methods. Furthermore, a detailed analysis research was conducted on the controller parameters and disturbances, considering equipment parameters, electricity pricing models, weather conditions, and load types. Notably, the MPC exhibited even greater performance improvements in scenarios involving real-time pricing, concentrated loads, and low heat pump capacity.

For central air-conditioning systems, the energy consumption of the chiller plant is huge. By optimizing the operation combination of chillers and the load distribution among chillers, it is possible to realize the energy saving while meeting the cooling demand for end users. Considering that the chiller performance is affected by the cooling capacity, supplying chilled water and returned cooling water temperature, an optimized model-based control strategy of chillers is designed based on the actual performances of chillers. In which, the actual maximum cooling capacity is used as constraint to reduce the search domain and accelerate optimization control calculation speed. Meanwhile, the performances of different optimization algorithms on the optimization results are discussed. Taking a cigarette factory in Wuhan as example to verify the optimized cluster control strategy, the result shows that compared to the traditional control strategy of chiller plant, both traversal optimization and genetic algorithm optimization strategies can achieve an energy-saving rate of about 20% in the entire cooling season.

An integrated system of vapor compression cycle and heat exchanger network can comprehensively use heat transfer and improve the temperature grade of thermal energy to achieve efficient heating and cooling. Con-structing an integrated system is necessary for saving energy and reducing CO2 emissions. Existing construction methods of integrated systems cannot cover a global optimal cycle structure and are unsuitable for integrated optimization with external heat exchanger networks. This study proposes an innovative integrated construction method, named the GraPHsep method, to realize one-step optimization of integrated structures of vapor compression cycles and external heat exchanger networks. The proposed method is verified by constructing an ideal cycle and efficient two-stage compression cycle under two heat reservoirs. Furthermore, the proposed method is applied to construct a combined cooling and heating system with waste-heat recovery. The results show that the three novel systems constructed using the GraPHsep method exhibit 38.6%-40.3% higher coef-ficient of performance (COP) than the existing energy-efficient system.

The supply-demand imbalance of electricity increases the operating burden on smart grids, decreases the average efficiency of power generation equipment, and threatens the safe operation of power grids. Residential air conditioning is a flexible load and a major consumer of electricity. Therefore, demand response control can be applied to air conditioners (ACs) to shift their peak energy consumption and save energy. Model predictive control (MPC) is an effective demand response control method. In this study, we analyze the cooling seasonal performance of an inverter AC with MPC. A time-varying MPC was designed and evaluated using a simulation testbed that was constructed using MATLAB. Subsequently, the energy, cost, and temperature control performances of the MPC were analyzed in detail from electricity pricing model, weather conditions and fluctuation of real-time price. The results show that compared to the proportional–integral–derivative (PID) control method, MPC can shift the peak-hour energy consumption by 6.34%–21.60% and reduce the total electricity costs by 13.44%–27.43%, while maintaining indoor thermal comfort during the whole cooling season, and Demand response with MPC control is very suited to hot weather conditions with highly fluctuating RTP. By applying MPC hybrid demand response under real-time price, there are better performances on peak shifting and cost saving.

面对新冠肺炎疫情的突然爆发,我国学者创造性地提出将体育场馆等大型公共建筑迅速改建为大规模收治轻症新冠肺炎确诊患者的"方舱医院"的技术方案,为新冠疫情防治做出了重要贡献.以夏热冬冷地区为例,分析了疫情较常发生的冬季大型体育场馆原有空调采暖方案的缺点,并根据病房区的安全性、热舒适保障性和系统节能性要求,提出了体育场馆改建方舱医院的空调系统更新方案.结合案例,从冷热源设备容量、空调箱送风量和换热能力探讨了新方案的可行性,从气流组织和舒适性两方面对比分析了新方案相对于原方案的优势,并论证了方案实际可用性.该工作对今后"平疫转换"体育场馆暖通空调系统的设计具有一定的参考价值.

超低能耗住宅具有高保温性与气密性,能显著降低空调供热能耗,是实现建筑节能的重要技术途径.采用被动式技术的超低能耗住宅与普通住宅的负荷特征不同,因此,按照传统方法设计和选型的热泵装置无法与超低能耗住宅的负荷匹配,成为超低能耗住宅的能源漏洞,发展适用于超低能耗住宅的热泵系统至关重要.以寒冷地区的典型城市北京为例,在模拟分析超低能耗住宅冷热负荷特征的基础上,分析与之相适应的热泵系统的特征.研究发现:超低能耗住宅的冷负荷是建筑负荷的主要部分,相比普通住宅,超低能耗住宅更易采用统一热泵设备满足全年供热与制冷的需求,且热泵的换热器宜按照制冷工况设计.

针对夏热冬冷地区现有方舱医院冬季供暖存在舒适性较低、污染物扩散差和能耗较高等不足,提出营造非均匀环境的"中温新风+辐射供暖"方案,设计了具有隔离病患和室内供暖双重功能的辐射隔板,以及可实现平疫转换的双温冷热源系统,在此基础上,结合案例对夏热冬冷地区新建平疫结合型体育馆的应用效果进行分析.结果表明:所设计的冷热源系统可实现多种工况之间的转换,相比"热风供暖"方案,冬季疫情期间采用"中温新风+辐射供暖"方案可减小27.4％的供暖负荷,且具有更高的热舒适度;在冬季作为方舱医院时,新建体育馆可节能21.7％,若进一步采用双温冷热源系统,其节能率可达38.5％.