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In this paper, we will focus on the retrofit of the ventilation system setup on the premise of reserving the original VAV system. Therefore, a combination of the existing VAV system and the dedicated outdoor air system (VAV-DOAS) is discussed. Here, the VAV system handles the indoor temperature and humidity without outdoor air supply, while the DOAS handles the indoor air quality with full outdoor air supply. The feasibility and economic analysis of this combined system are discussed by comparing it with the conventional VAV system operated in the multiple spaces equation-based strategy (MSE-V). The experimental results indicate that the presented VAV-DOAS can save 16% and 21% of energy consumption under winter and summer conditions in Dalian, respectively. Furthermore, this paper presents an economic evaluation method that can effectively evaluate the economy of a VAV-DOAS system retrofitted from the VAV system with different air conditioning loads and outdoor airflow demands. The economic analysis result shows that the retrofit system is economically feasible while the system is designed to serve for more than 12 years. [ABSTRACT FROM AUTHOR]

Cooling water (CW) system with strong nonlinear characteristics is a critical component of central air conditioning systems. Further, variable flow control of CW pumps is a critical measure for achieving energy-saving operation of CW systems. Proportional–integral (PI) control algorithm is widely used in the variable flow control of CW systems, and fuzzy control algorithm also plays a significant role in these systems. In this study, the application of fuzzy control and PI control algorithms is experimentally studied in a CW constant temperature difference system. The results indicate that the PI control algorithm of the CW system is simple in structure, and it only requires a reasonable PI parameter setting to achieve better control effect with high accuracy, good steady-state performance, and low energy consumption. However, the PI control algorithm is not adaptive for different working conditions. The fuzzy control algorithm has better dynamic performance and the adjustment curve is smooth with small fluctuations, although its control accuracy is not high. Over the same test period, the regulation frequency of the water pump under PI control algorithm is higher than that of the fuzzy control algorithm, and the CW system exhibits better stability under the fuzzy control algorithm. [ABSTRACT FROM AUTHOR]

For the sake of energy saving of variable air volume (VAV) system, this paper presents the a review of static pressure control around the optimization problem of static pressure reset in VAV air conditioning system. Then, main control methods of static pressure reset are described, and existing problems are analyzed and concluded. Finally, it is pointed out that the critical technology and the development trend of static pressure reset control. This overview is not intended to be an exhaustive survey on this topic, and any omission of other works is purely unintentional. [ABSTRACT FROM AUTHOR]

This paper analysis the operational electricity consumption of 2 office buildings in campus with VRF and centralized air conditioning system respectively, according to the monitoring data derived from a building energy monitoring and management system. Results show that both two air conditioning systems have great potential for energy saving, and the adjustability of VRF air conditioning system is better than that of centralized air conditioning system. Finally, the main effective factors of energy saving in the operation process of two air conditioning systems are pointed out. [ABSTRACT FROM AUTHOR]

The decoupling control of the quality and quantity parameters in heating, ventilation, and air conditioning (HVAC) systems with variable flow rates is critical to system performance optimization. However, the conventional optimization mechanism often employs an empirical fixed time interval to optimize the system periodically, which is not sufficiently flexible to handle load variations (e.g., outdoor weather changes and occupant turnover). Consequently, untimely or unnecessary optimization actions would aggravate the parameter coupling relationships. Therefore, a new optimization mechanism that considers coupling relationships must be studied to improve flexibility. In this paper, an event-driven optimization mechanism with adaptive intervals is proposed to improve the optimization efficiency and overall performance. The proposed strategy can take necessary optimization for the system while considering delayed response characteristics, which is beneficial to the system decoupling optimization control. To ensure the online feasibility of the proposed strategy, an online application mechanism with a double-loop framework is presented herein. The performance of the proposed method was verified experimentally using a practical variable flow rate HVAC system. Compared to the conventional time-driven optimization strategy, the proposed strategy presents a superior performance in terms of both decoupling and control effects, as well as a certain potential for energy savings. This research is significant and referable to related personnel (e.g., engineers and operators) to promote online decoupling control and intelligent optimizations with variable intervals and demand-based approaches. Meanwhile, the combination of dynamic diffusion characteristics and the double-loop control framework also provides a new orientation for the online optimization of practical systems. • Propose the sequential event-driven optimization (SEDO) strategy. • Medium dynamic diffusion properties in pipeline network are considered. • Compared with the time-driven optimization (TDO) strategy experimentally. • Proposed method performs superior decoupling and control effects. • Energy reduction of 4.03% can be achieved compared to the TDO strategy. [ABSTRACT FROM AUTHOR]

• Present theoretical models of dynamic diffusion properties in pipeline network. • An online application mechanism was proposed for HVAC system decoupling control. • Identify quantitative relations between delayed time and system dimension. • Optimal control timing is highly dependent on medium dynamic diffusion properties. The coupling characteristics of the control loops in the optimization process of the heating, ventilation, and air conditioning (HVAC) systems are critical to control effect and system stability. This is particularly prominent during the diffusion process of quality and quantity parameters in the pipeline network, because the obvious delayed response characteristics of medium parameters (e.g., temperature and pressure) will intensify the coupling issues. Therefore, the real-time diffusion characteristics of the medium parameters in a pipeline network must be studied to provide a reference for decoupling optimization. In this study, the dynamic characteristics of parameter diffusion in the pipeline network were studied essentially from the perspective of online decoupling control. An online application mechanism to improve the online feasibility of the dynamic characteristics in a practical system has been proposed, which also provides a new orientation for the online optimization of HVAC systems. Based on a simulation analysis, the qualitative and quantitative relations between the delayed response time and constitutive parameters in the pipeline network were identified, which is significant for the selection of the control period and optimization timing during the decoupling control in practice. Finally, the simulation results and online feasibility were verified experimentally in a real HVAC system. The experimental results show that the R2 for the hydraulic and thermal response models are 0.9596 and 0.9865, and the RMSE for predicting the flow rate and temperature are 0.0204 and 0.3791. This research is expected to provide considerable reference values for the time-oriented optimization of online decoupling control in practice and function as a theoretical foundation of our subsequent studies. [ABSTRACT FROM AUTHOR]

The automatic control of the existing indoor thermal environment is usually based on the user's set points without considering their real-time thermal feeling, causing an uncomfortable thermal environment and high energy consumption. In this study, the wristband device's physiological data is utilized to propose a thermal sensation prediction-based fuzzy control method of indoor temperature to improve the indoor environment's thermal comfort and achieve an energy saving of air-conditioning system. The linear regression model-based updating method is utilized to update the thermal sensation prediction model in real-time. Moreover, Mamdani fuzzy model and FFSI method are adopted to develop the fuzzy control algorithm for indoor temperature set point optimization. Several comparative experiments are performed to compare control performance, thermal comfort, and energy consumption of thermal sensation prediction-based control with thermal sensation feedback-based control and temperature set point-based control. The results show that thermal sensation prediction-based control can adjust the temperature set point by monitoring physiological data from the subjects without interfering with their regular works. It is also revealed that the thermal comfort performance of thermal sensation prediction-based control is superior to the temperature set point-based control, while it is similar to the thermal sensation feedback-based control. Furthermore, the proposed control approach provides 20.07% and 10.73% savings in daily energy consumption compared with the temperature set point-based control and thermal sensation feedback-based control, respectively. • A fuzzy control method of indoor temperature using physiological data is proposed. • The updating method is utilized to update the thermal sensation prediction model. • The proposed control method can improve thermal comfort. • The proposed control method can save the daily energy consumption. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) has the inspiring potential for artificial intelligence in heating, ventilation and air conditioning (HVAC) system with insufficient data labels. However, traditional TL-based methods are limited when applied across different conditions, systems, and operations.Unfortunately, public building HVAC systems encounter challenges related to data acquisition and richness, making it difficult to obtain data from similar HVAC systems conditions, scenarios and operations. It proposes a novel TL-based method that combines energy and mass balance constraint equation (EBCe) to diagnose the sensor faults in HVAC systems across different systems, conditions and operations.Firstly, it utilizes laboratory data as the source domain data and constructes EBCe based on the common physical laws of HVAC system to reduce the data differences between laboratory and public buildings. Then, an laplacian kernel domain-adaptive neural network (LkDaNN) is proposed to generalize more efficiently feature differences between the source domain data and target domain data. Finally, experiment analyzes the non-fault and four control-sensors fault under both cross-operation and non-cross operation conditions. The experimental results demonstrate that the EBCe-LkDaNN method achieves satisfactory fault detection and diagnosis (FDD) performance.The overall FDD accuracy of porposed method can reach 90.72 % and 88.64 % under different cross-operation, respectively. Practical application of the EBCe-LkDaNN strategy for HVAC sensor FDD are discussed at last. • This paper proposes a cross-domain fault diagnosis method. • The study enhances the generalizability of transfer learning. • The study enhances the interpretability of data-driven methods. • The study improves the fault diagnosis efficiency of transfer learning methods. [ABSTRACT FROM AUTHOR]

Due to the characteristic of large scale, multiple equipment, variable working conditions and complex debugging of air conditioning system in public buildings, the energy efficiency and operating level of the system are limited in the process of operation, and the actual operation effect will deviate from the design expectation. In addition, it is difficult to build an accurate mechanism model and to implement corresponding online control, so the commissioning for controller parameter tuning has to rely on human experience. Meanwhile, the response of indoor temperature to system regulation variables is different for different terminals due to lag characteristic, which would further affect the stability for the overall system. To solve the above problems, an adaptive predictive control method is proposed based on the physical-data driven hybrid model, which could not only realize online self-tuning of terminal controller but also improve control stability of terminal and overall system. Then, taking variable air volume air-conditioning system as the study object, the effectiveness of the proposed method is validated through comparative experiments. Results show that the proposed method can improve control stability of terminal and overall system. Achievements will have important theoretical significance and wide application prospect for improving the adaptability ability and the overall operating level of air-conditioning system in public buildings, which has complex and time-varying operation condition. [ABSTRACT FROM AUTHOR]

As the major power consumer in buildings, Heating, ventilation and air conditioning (HVAC) systems can significantly contribute to the economical operation of the power system during the peak periods in summer or winter, by flexibly responding to the grid demands. Indoor air temperature set-point resetting is a typical passive demand response (DR) control strategy that have potentials to be widely implemented by inverter air conditioners in residential buildings. However, in the existing small and medium-size public buildings, the compressor motor of HVAC systems mostly does not allow for stepless speed changes. This means that the indoor temperature set-point resetting strategy may results in frequent on/off switching of the chiller plant, leading to a compromised lifespan. Although rooftop photovoltaic systems (PVs) are expected to be widely adopted in new buildings, their potential for peak load shaving or valley load filling has received limited investigation. This study proposes a grey-box model-based demand side management (DSM) method for variable air volume systems (VAVs) and rooftop PVs in public buildings. The DSM method utilizes the particle swarm optimization (PSO) algorithm to determine optimal indoor air temperature and/or evaporator outlet water temperature set-point schedules for the VAVs, as well as optimal charging/discharging schedules for the storage battery of rooftop PVs. This approach aims to reduce the peak power demands of the integrated system without causing severe thermal discomfort, increasing chiller mechanical costs (refers to frequent on/off switching of the chiller plant in this paper), or overcharging/over-discharging of storage batteries. The results of case studies demonstrate that the strategy of co-resetting indoor air temperature – evaporator outlet water temperature set-points outperforms the indoor air temperature set-point resetting strategy in terms of both peak power demands reduction and mechanical cost saving when applied to the VAVs in low performance buildings. In comparison, for the VAVs installed in normal buildings, both the strategies yield significant decreases in peak power demands and mechanical cost by fully utilizing the thermal capacity of the building internal thermal mass. Furthermore, by implementing the DSM method for rooftop PVs, the integrated system can achieve zero or near-zero net power demands during the DR periods. The proposed DSM method can also be utilized in valley load filling programs to facilitate the accommodation of renewable energy. • A grey-box model of VAVs is developed and integrated with the building RC model. • A grey-box model-based DSM method is proposed for VAVs and rooftop PVs in buildings. • Impact of internal mass on peak shaving is quantified using the integrated model. • PSO is used to optimize the operating schedules of VAVs and rooftop PVs. • Tradeoff among peak shaving, thermal comfort and mechanical cost saving is realized. [ABSTRACT FROM AUTHOR]

With recent energy and environmental crises, the energy consumption prediction of air conditioning (AC) is essential. Notably, existing general prediction systems use the air temperature, real-time AC usage, time, occupancy, and AC historical energy consumption as inputs and employ feedforward neural networks. However, such systems face certain challenges. First, the input system may be difficult to reflect the real-time interaction among the building environment, occupants, and energy system; Second, the algorithms need to be upgraded to further improve accuracy, speed up convergence, and avoid overfitting. his paper introduces a novel parameter (group occupant behavior probability, GOBP) in the input system; and uses swarm intelligence algorithms with chaotic mapping and adaptive weight adjustment as optimization with LSTM and BP as the basic algorithms. The verification results obtained from five different buildings indicate the following: (1) The novel prediction system improved prediction performance with 20.08%–78.37 % of MAPE and 3.99%–53.28 % of CV-RMSE, however, it will increase 148.70s–1065.11s of calculation time. (2) For accuracy, using GOBP will improve the 0.30%–51.87 % and 0.18%–64.77 % by using swarm intelligence optimization algorithms. (3) This paper discusses the priority of selecting suitable inputs, algorithms, and optimization to combine a whole prediction system. • The definition of a simplified model using the group occupant behavior probability is proposed. • The chaotic mapping and adaptive weight adjustment are used for optimizing the swarm intelligent algorithms. • A reconstituted AC energy consumption prediction system is proposed with novel inputs and improved algorithms. • The reconstructed prediction system demonstrates improved prediction accuracy and data acquisition difficulty\. • The best combination of inputs and algorithms is discussed based on 60 groups of comparative experiments. [ABSTRACT FROM AUTHOR]