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Central air conditioning in large buildings is an important demand-response resource due to its large load power and strong controllability. Demand-response-oriented air conditioning load modeling needs to calculate the room temperature. The room temperature calculation models commonly used in the existing research cannot easily and accurately calculate the room temperature change of large buildings. Therefore, in order to obtain the temperature change of a large building and its corresponding power potential, this paper first proposes a building model based on CNN (convolutional neural network). Then, in order to fully apply the demand-response potential of the central air conditioning load, this paper puts forward an evaluation method of the load-reduction potential of the central air conditioning cluster based on pre-cooling and develops an economic load-reduction strategy according to the different energy consumption of different buildings in the pre-cooling stage. Finally, multiple building examples with different building parameters and temperature comfort ranges are set up, and the economic advantages of the proposed strategy are illustrated by Cplex solution examples. [ABSTRACT FROM AUTHOR]

This study presents an entropy analysis of the losses in different sections of a central air conditioner. The methodology for determining the losses using entropy analysis is presented in this work. The temperature and humidity of the outside and recirculating air were measured, and losses due to mixing of air flows, cooling in the evaporator section, and heating in the condenser section were calculated. A comparative analysis of the results was performed, and the distribution diagrams of the component losses were presented. Conclusions regarding a loss-reduction method were proposed. [ABSTRACT FROM AUTHOR]

As energy plays a fundamental role in our modern life and most of a building's energy is used for air conditioning, understanding the sustainable regulation theory of central air conditioning remains a significant scientific issue. In view of three shortcomings of existing energy-saving regulation methods of central air conditioning: (1) few studies on low-latency, high-reliability, and safer energy-saving control operation modes, (2) lack of consideration for human comfort, and (3) insufficient analysis of the comprehensive impact of the human–machine–environment, this paper proposes an energy-saving control framework of central air conditioning based on cloud–edge–device architecture. The framework establishes a prediction model of human comfort based on recurrent neural network. An intelligent energy-saving control strategy is proposed to ensure indoor personnel's thermal comfort, considering the human–machine–environment factors. This study provides a basis for better understanding the sustainable control theory of building central air conditioning. Finally, the experiment proves that the proposed method can effectively reduce the energy consumption of central air conditioning. Compared with traditional regulation approaches, the proposed real-time control strategy can save up to 91% of energy consumption, depending on the environment, and advance control strategies can save an average of 4%. [ABSTRACT FROM AUTHOR]

As the energy demand escalates with the evolution of smart buildings, the need to conserve energy and reduce consumption in central air conditioning systems—the primary energy consumers in buildings—has become increasingly critical. This study develops a multi-objective optimization framework that balances thermal comfort with energy consumption in central air conditioning systems. The system comprises cooling towers, water-cooled chillers, and air-handling units, among other components. For modeling, a mechanistic model for accurately characterizing cooling tower performance is advocated, a three-damper control strategy for air-side economizers is used, and an occupancy-dependent dynamic ventilation strategy is developed. Additionally, this work effectively separates the multi-objective optimization problem from the optimal damper control strategy issue, simplifying the solution process. Simulation results indicate that the damper control strategy adopted can reduce the fan energy consumption of air handling units by 5.622% while preserving indoor air quality. The adaptive ventilation strategy can lower overall system energy use by 10.373%. Implementing this framework not only ensures thermal comfort but also significantly lowers system energy consumption by 48.091% compared to the baseline case. • A more accurate cooling tower performance model is advocated. • The three-damper strategy lowers the flow resistance coefficient. • A dynamic ventilation strategy reduces system energy use. • A multi-objective framework balances energy consumption and comfort. [ABSTRACT FROM AUTHOR]

Chilled water systems in large-scale central air conditioning consume more than 30 % of the total energy. To reduce this energy consumption, a model can be used to explore the optimal operating parameters of chilled water systems. The chilled water system is a multivariable highly coupled nonlinear system. The type and number of variables change with the hydraulic characteristics of the piping network and the operating data sparsity is high. For these reasons, data-driven methods are not suitable for modeling chilled water systems. Therefore, there is an urgent need to develop a model that is not constrained by the type and number of variables and can establish a complete data structure. According to the principles of differential pressure equilibrium, flow conservation, and thermal equilibrium, this study establishes a physical model of the hydraulic–thermal coupling of chilled water systems. The hydraulic–thermal characteristics of chilled water systems under multiple operating conditions were investigated, and the reliability of the model was verified through experiments. Furthermore, the difficulty of effective modeling owing to dynamic changes in the types of variables and lack of operating data were addressed. The study showed that the model has a wide range of applications, high reliability, and high computational efficiency. • Physical models are not affected by changes in the type and number of variables. • Enable model convergence and computational efficiency by building a database. • Model applications to big data simulation; sensitivity analysis; boundary problems, etc. • High reliability of the model verified by experiments. [ABSTRACT FROM AUTHOR]

Central air conditioning in large buildings is an important demand-response resource due to its large load power and strong controllability. Demand-response-oriented air conditioning load modeling needs to calculate the room temperature. The room temperature calculation models commonly used in the existing research cannot easily and accurately calculate the room temperature change of large buildings. Therefore, in order to obtain the temperature change of a large building and its corresponding power potential, this paper first proposes a building model based on CNN (convolutional neural network). Then, in order to fully apply the demand-response potential of the central air conditioning load, this paper puts forward an evaluation method of the load-reduction potential of the central air conditioning cluster based on pre-cooling and develops an economic load-reduction strategy according to the different energy consumption of different buildings in the pre-cooling stage. Finally, multiple building examples with different building parameters and temperature comfort ranges are set up, and the economic advantages of the proposed strategy are illustrated by Cplex solution examples.

In order to conduct a data-driven load forecasting modeling and its application in optimal control of air-conditioning system, this study used a hotel's central air conditioning system as the research object. Based on the data of the hotel energy management system, the load-forecasting model of the central air conditioning system based on support vector regression (SVR) was established by MATLAB. Based on the working principle of a chiller, chilled water pump, cooling water pump, and cooling tower, the energy consumption models were established, respectively. Finally, based on the load-forecasting results and the equipment energy consumption model, the energy consumption optimization objective function of the hotel water system was established, the objective function was solved to optimize the operating parameters of the water system at different load rates, the operation control strategy for each piece of equipment was obtained, and the energy-saving analysis was carried out. The results show that in the range of a load rate of 25~90%, the optimization strategy has an energy-saving effect, and the system's energy-saving rate is the highest when the load rate is 25.4%. The average energy-saving rate of the system is 12.4%. [ABSTRACT FROM AUTHOR]

Taiwan is located in a subtropical region with high temperatures and humidity. Office spaces are located in air-enclosed rooms in buildings, where doors and windows remain closed and only a central air-conditioning system provides temperature adjustment and ventilation. Most offices in this area have office seating areas, document storage areas on both sides of the office, and multi-function devices, which can cause sick office syndrome in the employees. This study applied environmental monitoring technology to analyze the architectural form and indoor and outdoor air quality to propose improvement strategies addressing indoor temperature, relative humidity and air quality. Quality indices were used created to evaluate the improvement efficiency. The analysis results showed that the indoor temperature and relative humidity in staff seating areas can be effectively improved. The statistical analysis results of improved efficiency for PM2.5, PM10 concentrations and total suspended particulates showed that the average indicator values have been raised from 0.05 to 1.5, 2.45 to 4.02 and 0.91 to 3.54, respectively, for staff seating area and −0.01 to 2.82, 0.15 to 3.91 and 1.25 to 7.25, respectively, for photocopier areas. The ambient air quality of this office space has been significantly improved. This study can serve as an example of air quality improvement in traditional common office spaces. [ABSTRACT FROM AUTHOR]

AIM: To estimate the prevalence of and risk factors for dry eye disease (DED) in young and middle-aged office employee in Xi'an. METHODS: This cross-sectional study of the prevalence of and risk factors for DED investigated 486 young and middle-aged Chinese office employee in Xi'an. DED symptoms and potential risk factors were assessed using the ocular surface disease index combined with a risk factors questionnaire, and tear function was evaluated using the tear film break-up time and Schirmer's test. Possible risk factors for DED were estimated by binary Logistic regression analysis. RESULTS: DED was diagnosed in 100 females and 96 males, giving a prevalence of 40.3% [95% confidence interval (CI)=36.0%-44.7%]. The multivariate binary Logistic regression model indicated that the possible risk factors for DED were being female (OR=1.592, 95%CI=1.034-2.451, P=0.035), being aged ≥40y (OR=1.593, 95%CI=1.034-2.454, P=0.035), using a VDT daily for >6h (OR=1.990, 95%CI=1.334-2.971, P=0.001), the presence of central air conditioning (OR=1.548, 95%CI=1.053-2.276, P=0.026), and self-reported dryness of the mouth and nose (OR=1.589, 95%CI=1.071-2.357, P=0.021). CONCLUSION: There is a high prevalence of clinically diagnosed DED in young and middle-aged video display terminal (VDT) users. Interventions against the modifiable risk factors should be taken to prevent the occurrence and development of DED in this population.

Central air conditionings (CACs) deployed in large-scale commercial buildings have consumed a large amount of energy in cities. Growing attention has been expressed in their operating efficiency improvement and demand response (DR) participation to facilitate energy conservation and supply–demand balance. To tackle these challenges, a chance constrained economic dispatch approach is developed for CACs, aimed at optimizing their operation scheduling while accounting for DR participation. Firstly, the economic dispatch framework is established to harness capacity reserves, thermal inertias, and chance constraints to deal with cooling load uncertainties, accompanied by a three-stage DR strategy aimed at augmenting response potential. On this basis, the operation scheduling of CACs is formulated as an optimization problem to minimize overall operating costs considering DR incomes. To solve the problem, the energy-temperature transformation model is developed to quantify available thermal inertias and DR capacities within user comforts. Additionally, chance constraints are converted to specific reserve requirements, to prevent inappropriate dispatches caused by severe but rare forecast errors. Furthermore, the feasible regulation region is designed to coordinate capacity reserves and thermal inertias in response to cooling load forecast errors. Lastly, case studies are conducted using realistic data from commercial buildings, validating the effectiveness of dispatch on operating efficiency improvement, forecast uncertainty accommodation, as well as contributions to DR provision. [ABSTRACT FROM AUTHOR]

Virtual power plant is an integrated technology and operation mode to realize air-conditioning load participating in power system operation, further benefitting low carbon renewable energy applications. However, the principle of multi-system coupling in central air-conditioning poses a challenge to normal load regulation. Besides, the uncertainties of demand-side resources bring risks to the operation of virtual power plant. In this paper, the regulation characteristics of central air conditioning are obtained by experiment, while the potential of central air conditioning is quantified by a thermodynamic model, further resulting in the central air conditioning could be transformed into a virtual unit model. Then the dynamic capacity optimization strategy is formulated based on the risk measurement theory, while the generation task decomposition strategy is also formulated based on the equal increase rate criterion, thus forming a two-tier operation strategy of virtual power plant. Finally, illustrative case study is constructed to quantitatively analyze the power generation capacity and effectiveness of the virtual power plant. The effectiveness and practicability of the proposed strategy is also verified to benefit low carbon energy applications.

Taiwan is located in a subtropical region with high temperatures and humidity. Office spaces are located in air-enclosed rooms in buildings, where doors and windows remain closed and only a central air-conditioning system provides temperature adjustment and ventilation. Most offices in this area have office seating areas, document storage areas on both sides of the office, and multi-function devices, which can cause sick office syndrome in the employees. This study applied environmental monitoring technology to analyze the architectural form and indoor and outdoor air quality to propose improvement strategies addressing indoor temperature, relative humidity and air quality. Quality indices were used created to evaluate the improvement efficiency. The analysis results showed that the indoor temperature and relative humidity in staff seating areas can be effectively improved. The statistical analysis results of improved efficiency for PM2.5, PM10 concentrations and total suspended particulates showed that the average indicator values have been raised from 0.05 to 1.5, 2.45 to 4.02 and 0.91 to 3.54, respectively, for staff seating area and −0.01 to 2.82, 0.15 to 3.91 and 1.25 to 7.25, respectively, for photocopier areas. The ambient air quality of this office space has been significantly improved. This study can serve as an example of air quality improvement in traditional common office spaces.

With the large scale use of central air conditioners in colleges, accurate measurement and reasonable billing of central air conditioners has become an increasingly concerned issue for administrators and students. In order to solve the conflicts of interests between managers and users, a college dormitory' s central air conditioning measurement method based on Gaussian mixture model is proposed in this study. The method is based on the load characteristics of centrat air conditioning in college student dormitory, and uses Gaussian mixture model lo analyze the probability and statistical characteristics of air conditioning load. By measuring the electricity consumption of central air conditioning and terminal fans, the calculus of time differences method based on its statistical characteristic is adopted to achieve split charging. The proposed method not only realize accurate metering and charge, hut also combines the intelligent power management system to make the electrieity process more transparent and has certain energy - saving effects. [ABSTRACT FROM AUTHOR]

With the gradually widely usage of the air conditioning (AC) loads in developing countries, the urban power grid load has swiftly increased over the past decade. Especially in China, the AC load has accounted for over 30% of the maximum load in many cities during summer. This paper proposes a scheme of constructing a virtual peaking unit (VPU) by public buildings' cool storage central AC (CSCAC) systems and non-CSCAC (NCSCAC) systems for the day-ahead power network dispatching (DAPND). Considering the accumulation effect of different meteorological parameters, a short term load forecasting method of public building's central AC (CAC) baseline load is firstly discussed. Then, a second-order equivalent thermal parameters model is established for the public building's CAC load. Moreover, the novel load reduction control strategies for the public building's CSCAC system and the public building's NCSCAC system are respectively presented. Furthermore, based on the multiple-rank control strategy, the model of the DAPND with the participation of a VPU is set up. The VPU is composed of large-scale regulated public building's CAC loads. To demonstrate the effectiveness of the proposed strategy, results of a sample study on a region in Nanjing which involves 22 public buildings' CAC loads are described in this paper. Simulated results show that, by adopting the proposed DAPND scheme, the power network peak load in the region obviously decreases with a small enough deviation between the regulated load value and the dispatching instruction of the VPU. The total electricity-saving amount accounts for 7.78% of total electricity consumption of the VPU before regulation.

In order to improve the degree of renewable energy integration, the demand response resource, especially on the air conditioning load side, has received attention. Currently, most of the control modes for air conditioners are centralised control, which is difficult to be applied to widely distributed air conditioning. The current studies focus on central air conditioning, but there are few studies on split‐type inverter air conditioning, even though inverter air conditioning has become the most common split air system on the market. With this background, this study designs a consensus control strategy on the basis of distributed control mode, which for inverter air conditioners. The strategy is used for renewable energy integration in the studied region. The test results of various cases reveal the following findings: (i) the strategy adapts to the widely distributed air conditioning, (ii) the strategy can guarantee a fair allocation of power imbalance and user comfort, (iii) the control strategy can adapt to the uncertainties of air conditioners and the urban microclimate while efficiently consuming renewable energy. Therefore, the proposed consensus control strategy has practical value. [ABSTRACT FROM AUTHOR]

The article presents an entropy-statistical analysis of losses in the mixing section of a central air conditioner. The temperature and humidity of the outdoor and recirculating air were measured. The methodology for determining losses using the entropy-statistical method is presented in the work. The calculation of losses in the mixing section of the central air conditioner due to the mixing of air flows depending on the flow rate of the supply and recirculation air as well as due to hydraulic losses. A comparative analysis of the results is carried out and the distribution diagrams of the component losses are presented. Proposed conclusions regarding the way to reduce losses.

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189 conditioned residential single-family and multi-family homes in the cooling-dominated climate of Texas were instrumented with home energy management systems (HEMS) to collect sub-metered data on HVAC operations. This study analyses the HVAC operation from these homes over a 1-year period to determine the duty cycles of the HVAC systems. This includes annual, monthly, and hourly HVAC ON-OFF operation patterns. Regression analysis was used to determine the relationship to HVAC energy use and whole-home energy use, and the influence of building and occupant characteristics. HVAC runtimes are found to be approximately 20% per year, but vary, depending on the season and time of day. Daily and monthly runtime fractions are lowest (10%) at average outdoor temperatures of 15°C, and increase with increasing or decreasing temperature. Hourly runtime peaks at 7 pm in the cooling season, while in the heating and transition seasons, it peaks at 7 am. The number of occupants and the indoor cooling set point temperature were found to most strongly influence the HVAC runtime. The results are formatted to be used in various building and indoor air quality applications where the studied phenomena are influenced by HVAC operation. [ABSTRACT FROM AUTHOR]

• A quantitative assessment method of CACs' regulation capacity is proposed. • An adaptive method is proposed to allocate regulation requirements among CACs. • A fluctuation suppression control is adopted to guarantee the regulation accuracy. • An online control method is developed to ensure occupants' comfortable temperatures. The increasing renewable energies bring more generation fluctuations to power systems, which puts forward a higher requirement on regulation services for maintaining the system balance. Existing traditional generating units probably cannot provide sufficient regulation services to solve this challenge. With the progress of information and communication technologies, it is possible to control demand-side flexible loads to provide regulation services. Among various flexible loads, central air conditionings (CACs) have huge regulation potential, because CACs account for about 40% of the total energy consumption in buildings. However, the operation of CACs is a complicated dynamic process with multiple subsystems and independent control loops. It is difficult to accurately regulate CACs to reach the required regulation services within the comfortable indoor temperature. To address this issue, this paper focuses on the quantitative assessment and two-layer coordinated controls of CACs to provide regulation services for the power system. Firstly, the thermal-electrical model of the CAC is established to describe the dynamic operation process of subsystems' characteristics. On this basis, a quantitative assessment method of CAC's regulation services is proposed by discretizing the thermal-electrical operation characteristics. Then, to aggregate multiple CACs to provide significant regulation capacities, an adaptive allocation method is proposed to satisfy the power system's regulation requirement based on different CACs' available regulation potential. Furthermore, an online distribution control method is developed to regulate each individual CAC's cooling capacity for guaranteeing the regulation accuracy and different occupants' comfortable temperatures. The effectiveness of the proposed models and methods is illustrated by numerical studies. [ABSTRACT FROM AUTHOR]