Energy Consumption Of Air Conditioning System Research Articles

Energy-saving regulation methods and energy consumption characteristics of office air-conditioning loads in hot summer and cold winter areas

The air-conditioning system of an office building may have much higher operational energy consumption than its designed value due to the actual use behavior which significantly deviates from the predicted one. To precisely analyze the actual air-conditioning energy consumption, in this paper, based on the impact of occupant air-conditioning behavior on the energy consumption of air-conditioning systems, a Supply-Side Cooling Load Regulation Method (SSRM) is proposed, and its air-conditioning energy consumption analysis model is established. The energy consumption characteristics of the air-conditioning system under SSRM were analyzed, with the assumption that the example office building is located in a hot summer and cold winter areas. The results indicate that the indoor air conditioning set temperature and the actual fresh air volume are important factors affecting the variation of the air conditioning system cooling load under the Demand-Side Cooling Load Regulation Method (DSRM) method, whereas under SSRM, the air conditioning system cooling load is not affected by the variation of the indoor set temperature and the fresh air volume; meanwhile, regulating human air-conditioning use behaviors is the key point of reducing air-conditioning cooling load. The increasing fresh air volume from 30 m3/(p∙h) to 90 m3/(p∙h) can result in about a 5°C increase in the average daily indoor air temperature in the rooms used by SSRM, and it also causes an increase in the total air-conditioning load in the rooms under DSRM by more than 45 kWh/day. Further, compared to the DSRM, the SSRM can well regulate occupant air-conditioning use behaviors, which reduces energy consumption by more than 35% in the summer.

A comparative evaluation on energy consumption of a building using bio-based and paraffin-based phase change materials integrated to external building envelope

Using thermal energy storage such as phase change materials (PCMs) is a suitable solution to reduce energy consumption in order to maintain thermal comfort conditions inside the building. There is a wide range of phase change materials according to their different characteristics such as melting temperature, freezing temperature, latent heat capacity, specific heat value of solid and liquid phase, thermal conductivity coefficient, and charge and discharge range, which makes it difficult to choose the right PCM. In this research, two common groups of phase change materials that are used commercially are studied and after performing a sensitivity analysis on them, a suitable temperature range is selected. Then the selected PCMs are placed in different parts of the external envelopes of the building and various parameters such as air conditioning system energy consumption, thermal comfort, heat balance, etc. are examined. It should be noted that in this study, the use of PCMs in buildings with cooling and heating systems is investigated. In order to carry out the simulation, a two-story office building located in Arak, Iran was modeled in Design Builder software along with all the details. The outputs show that if Bio PCM is used in the outer layer of the wall in the summer season, it is possible to save up to 154 w/m2 in the energy required for cooling the building. While the use of Bio PCM M91 Q21 material in the inner layer of the wall will reduce the load of the cooling system by 246.15 Wh/m2. Also, if RT PCM is used, it is possible to reduce the heat load by 80%. Furthermore, by analyzing the optimal cases, it can be concluded that increasing the thickness of the PCM layer cannot effectively improve the thermal performance of the building. On the contrary, their reduction in some cases leads to a reduction in energy demand in the building. Finally, in order to generalize the results, the optimal situation was compared with two similar climates of Arak city.

Granularity Optimization for Efficient Energy Consumption Monitoring in Subway Stations for Enhanced Energy Management

Efficient energy data management forms a critical foundation for unlocking the carbon reduction potential of subway systems, holding significant importance in advancing urban processes towards lowcarbon and clean environments. Low-precision sampling is difficult to reflect the actual energy consumption of the station, and high-precision sampling has high requirements for the data storage and transmission capacity of the monitoring system. In order to determine the appropriate sampling accuracy, this study analyses the power load fluctuation characteristics of stations on a subway line in the North China Plain and optimizes the sampling granularity for achieving minimal data storage requirements while effectively capturing energy consumption fluctuation information. The findings indicate that a higher sampling granularity for power load monitoring is advisable during the summer to capture the frequent fluctuation characteristics of ventilation and air-conditioning system energy consumption. For a typical underground station, it is recommended to use a sampling interval of 5 min in summer and 15 min or longer in other seasons. For a typical elevated station, a sampling interval of 10 min is recommended in summer, and 20 min or longer in other seasons.

The Review of the Application of the Heat Pipe on Enhancing Performance of the Air-Conditioning System in Buildings

An air-conditioning system (ACS), which consumes large amounts of high-grade energy, is essential for maintaining the indoor thermal environment of modern buildings. However, an ACS consumes almost half of the total energy of the building. Therefore, it is necessary to reduce the energy consumption of the ACS to promote energy conservation and emission reduction in the building sector. In fact, there is an abundance of waste heat and low-grade energies with the potential to be utilized in ACS in nature, but many of them are not utilized efficiently or cannot be utilized at all due to the low efficiency of thermal energy conversion. Known as a passive thermal transfer device, the application of a heat pipe (HP) in the ACS has shown explosive growth in recent years. HPs have been demonstrated to be an effective method for reducing building cooling and heating demands and energy consumption in ACS with experimental and simulation methods. This paper summarizes the different HP types applied in the ACS and provides brief insight into the performance enhancement of the ACS integrated with HP. Four types of HPs, namely tubular HP (THP), loop HP (LHP), pulsating HP (PHP) and flat HP (FHP), are presented. Their working principles and scope of applications are reviewed. Then, HPs used in natural cooling system, split air conditioner (SAC), centralized ACS (CACS) and cooling terminal devices are comprehensively reviewed. Finally, the heat transfer characteristics and energy savings of the above systems are critically analyzed. The results show that the performance of the HP is greatly affected by its own structure, working fluid and external environmental conditions. The energy saving of ACS coupled with HP is 3–40.9%. The payback period of this system ranges from 1.9–10 years. It demonstrates that the HP plays a significant role in reducing ACS energy consumption and improving indoor thermal comfort.

Real-time energy consumption prediction method for air-conditioning system based on long short-term memory neural network

With increasing energy consumption, how to achieve the energy-saving operation of air-conditioning systems is crucial for improving the energy efficiency of buildings. The accurate and reliable energy consumption prediction of air-conditioning systems can be useful for optimizing the energy supply and equipment operation strategies. However, most existing studies focus on the prediction of the long-term energy consumption of air-conditioning systems, which usually exceeds the individual control time steps of the air-conditioning system. If the energy consumption of air-conditioning systems in the next control step can be predicted, then the least energy-consuming operating strategy can be selected at the beginning of each control step by relying on the predicted energy consumption, thereby achieving greater energy conservation. This paper proposes a real-time energy consumption prediction model for air-conditioning systems based on a long short-term memory neural network. The correlation between the energy consumption of air-conditioning system and input variables can be obtained by calculating the Spearman correlation coefficient. The predicted model results show that the normalized root mean square error of the prediction model is 0.0429 when the indoor air temperature, condenser inlet temperature, and historical energy consumption are set as inputs to predict the energy consumption of the air-conditioning system for the next 15 min. Meanwhile, the energy consumption at a certain indoor temperature setpoint can be predicted by the prediction model to aid in selecting a more appropriate indoor temperature setpoint, which can reduce energy consumption by optimizing the operation strategy of the air-conditioning system at each control step.

A hybrid WOA-SVM based on CI for improving the accuracy of shopping mall air conditioning system energy consumption prediction

Energy consumption prediction is of great practical significance for the study of energy-saving scheduling of building air-conditioning systems. However, there are two problems in prediction models: (1) A single prediction model is difficult to solve the nonlinear problem of building energy consumption data. (2) A single prediction result can hardly reflect the uncertainty in the building operation process. Therefore, in this study, a hybrid prediction model with an improved whale optimization algorithm- optimization support vector machine (IWOA-SVM) is proposed based on the air conditioning system of a shopping mall in Xi'an. The WOA is improved using the opposition-based learning strategy (OBL), differential evolution algorithm (DE), and introducing a control parameter λ. The Confidence Interval (CI) of the model fitting error is used to represent the prediction results. The results show that the root mean square error (RMSE) of the optimized model is reduced by 9.5, and the (RMSE) of the total energy consumption prediction value after adding CI is >1.68. The method achieves higher model accuracy, reflecting the operational fluctuations of the building.

Numerical Investigation on the Influence of Operation Mode of the Air-Conditioning and Oxygen Supply System on Energy Consumption of Plateau Train

Problems of low temperature, low pressure, low oxygen, and high carbon dioxide (CO2) concentration in the air-conditioning (AC) trains of the Qinghai-Tibet railway can affect the health and comfort of passengers and cause altitude sickness. When the Qinghai-Tibet railway train runs in high-altitude areas, it is necessary to supply oxygen and introduce fresh air to meet the limited oxygen (O2) and CO2 partial pressures (PPs) in the carriage. In this study, a numerical analysis of the correlation between the CO2 PP and O2 PP in AC trains and the air supply parameters (ASPs), fresh air volume (FAV), and oxygen supply volume (OSV) along the Qinghai-Tibet line in summer was conducted. The results show that the influence of the FAV on the energy consumption of the air-conditioning system (ACS) in different running areas is inconsistent, whereas the influence of the oxygen supply system (OSS) on energy consumption is significant. During the oxygen supply period, the FAV had the opposite effect on energy consumption of ACS and OSS. The energy consumption of the OSS was approximately five times that of the ACS. By studying the correlation between the internal environment and ASP of trains running in different regions, the operation modes of ACS and OSS can be reasonably set, which can effectively reduce energy consumption by 20–50%.

Thermal sensation and occupancy-based cooperative control method for multi-zone VAV air-conditioning systems

Most existing multi-zone variable air volume (VAV) air-conditioning systems rely on manual adjustment or a constant temperature set point, with no consideration for users' thermal sensation. VAV terminals operate regardless of whether there is a demand for cooling in the zone, rather than according to the actual occupancy situation between multiple zones, resulting in poor indoor thermal comfort and the high energy consumption of air-conditioning systems. This paper proposes a thermal sensation and occupancy-based cooperative control (TSOCC) method for multi-zone VAV air-conditioning systems in order to provide an indoor thermal environment more in line with the real-time needs of personnel, while also increasing cooperative control between multiple zones to ensure that VAV terminals do not operate unnecessarily when zones are unoccupied. To evaluate the control performance of this method, a simulation platform is built, case simulations with different zone occupancy rates are carried out and various comparisons and analyses are made with temperature set point-based control. The results show that the TSOCC method for multi-zone VAV air-conditioning systems can provide an indoor thermal environment more in line with the thermal comfort needs of the personnel and save 6–14% of daily system energy consumption compared with the temperature set point-based control strategy, while the system's energy-saving rate increases as the zone occupancy rate decreases, marking a change from whole zone-oriented cooling to zone-oriented cooling and providing more accurate air-conditioning services for indoor personnel.

A behavior-orientated prediction method for short-term energy consumption of air-conditioning systems in buildings blocks

The short-term prediction of air-conditioning (AC) energy consumption is a crucial part of building operation optimization and demand-response strategies. However, the AC energy consumption in some types of buildings can be highly stochastic due to arbitrary occupants' behavior of AC usage, which make it had to predict. Currently, very few prediction models considered the impacts of stochasticity related to occupants’ behavior of AC, which leads to less accuracy. To predict the AC energy consumption under stochastic energy use scenarios, this study proposes a behavior-orientated prediction method for short-term AC energy consumption with a case study on teaching building blocks. Firstly, cluster analysis is employed to identify typical patterns to quantify stochastic AC energy use. Then, the AC usage rate is predicted based on weighted k-Nearest-Neighbors method, which can provide a precise prediction of stochastic AC usage rate. Based on these, RandomForest method is used to develop a basic prediction model of AC energy consumption. The importance of each variable is also evaluated. Finally, an enhanced part of prediction is implemented by support vector machine to achieve higher accuracy under stochastic AC energy use scenarios. The results show that both proposed models can accurately predict the AC energy consumption, while the proposed enhanced model can reap significant accuracy improvement under the more stochastic scenarios. This study provides a new approach to predict AC energy consumption in buildings with stochastic AC energy use, which can match the real-time amount with good accuracy. The proposed model can provide an accuracy reference for the demand response strategies and optimization of AC systems operation.

Optimal chiller loading solution for energy conservation using Barnacles Mating Optimizer algorithm

This paper proposes an application of evolutionary optimization algorithm, Barnacles Mating Optimizer (BMO) to solve the optimal chiller loading (OCL) problem for minimization of the power consumption in the multi chiller system. BMO mimics the mating behaviour of barnacles in nature. To generate new off-springs, the concept of Hardy–Weinberg principle is adopted for exploitation process and the sperm-cast process is treated as exploration behaviour of BMO. OCL on the other hand is the problem of finding the combination of partial load ratio of each chiller to obtain the minimum energy consumption in air-conditioning system. To demonstrate the effectiveness of proposed BMO, it is tested on three different chiller systems. The obtained results from BMO are compared with other well-known optimization algorithms in the literature. The obtained comparison results indicate that proposed BMO is effective to reach minimum energy consumption for solving the OCL problem.

Modeling of the effect of nano-enhanced phase change material on the performance of a large-scale wallboard

The main goal of this present study is to investigate the effects of the application of PCM and Nano-enhanced PCM as wallboard on the thermal behavior of a room. For this purpose, a room was modeled in two dimensions under Tehran's summer weather conditions through computational fluid dynamics (CFD). The effect of using the PCM as a wallboard in the southern wall in both pure and enhanced with nanoparticles was investigated. The indoor temperature, the wall surface temperature, and the interior wall heat flux, in both cases, were reported and compared. At the end of this study, the acquired results were compared with the pre-modified room, and thermal improvement was reported. The results indicate that the use of solid nanoparticles in PCM reduces the energy consumption of air-conditioning system by 7.4% compared to the conventional room. In the case of the Nano-enhanced PCM wallboard, the room has better thermal performance than the pure PCM, with 4.37% more energy storage, about 0.273 reductions in temperature decrement factor, and a 21.6 min increase in the time delay to peak temperature. Compared to the conventional room and room with pure PCM, the room’s temperature fluctuation, modified by Nano-enhanced PCM, reduces by 52% and 31%, respectively. This study's obtained results could help the researchers and designers have a more appropriate PCM selection for building ventilation system applications.

Study on simulated natural wind based on spectral analysis

A dynamic thermal environment meeting human health requirements can be created with natural wind simulated by artificial means. Simulating natural wind can provide an innovative air-conditioning mode that reduces the energy consumption of air-conditioning systems and avoids the body-resistance decrease and body-temperature regulation mechanism failure that limit the traditional steady air-conditioning mode. In this paper, new methods for simulating natural wind are presented in which the controllable fluctuation of mechanical wind is improved to make its characteristics more similar to those of natural wind. Experiments carried out to verify the effectiveness of these mechanical-wind methods are reported. A self-operated air supply device improving the fluctuation of the air supply is proposed, which can increase the value of the characteristic parameter β (the negative slope of the double-logarithmic power spectrum curve) to 1.2 when the wind speed is greater than 1 m/s. With a second proposed device based on the gas-washing process, this value can reach 1.4 when the wind speed is greater than 0.5 m/s. In summary, this paper provides simple solutions for simulating natural wind.

Simulation-based optimization of the control strategy of variable-frequency chilled water pump in data center: A case study in Beijing

Reducing the energy consumption of air-conditioning system is one of the important means to improve the operation energy efficiency of data center, and the energy consumption of water pump accounts for a relatively high proportion of the energy consumption of air-conditioning system. This paper studied the reduction of operation energy consumption of water pump from the perspective of optimizing its control strategy. Based on the simulation model of the case system, this paper simulated the operation energy consumption of the chilled water pump when two control strategies (the constant DP control and the constant DT control of the main water supply-return pipeline) were used under different load demand. The calculation results show that both two control strategies can reduce the energy consumption of the chilled water pump compared with non-control, and the constant DT control is better. In addition, under two control strategies, the load distribution characteristics of system have an impact on the working state point and energy consumption of the chilled water pump.

On-site performance investigation of liquid-desiccant air-conditioning system applied in laboratory rodent room: A comparative study

Considerable ventilation rates in laboratory animal rooms indicate huge energy consumption of air-conditioning systems. The liquid-desiccant air-conditioning (LDAC) system is regarded as an energy-efficient system, which can help to save energy in laboratory animal rooms. However, limited studies have applied LDAC systems to this kind of rooms for energy conservation. In this study, a demonstration project is focused on, wherein both the LDAC system and conventional vapor-compression system are used in the laboratory rodent center. On-site measurements were conducted to reveal the energy performance difference between the two systems. The test results reveal that the considerable cooling loads of two rodent rooms air-conditioned by the conventional system and LDAC system are 482.1 W/m2 and 576.8 W/m2, respectively, under the summer conditions in Beijing. The LDAC system can avoid reheating and recover cooling from the exhaust air from indoors. Thus, the coefficient of performance (COPsys) of the LDAC system ranges 3.5–4.5, whereas the COPsys of the conventional system only ranges 1.8–2.3 during the test period. During the entire cooling season, an electric power saving rate of 37.3% can be achieved by the LDAC system. Moreover, although both systems can satisfy all the environment control requirements, the LDAC system is beneficial for particulate matter filtration, yet slightly unfavorable for reducing indoor ammonia concentration, compared to the conventional system.

Operation and performance of VRF systems: Mining a large-scale dataset

The energy consumption of air-conditioning systems has gained increasing attention as it contributes significantly to the global building energy use. The variable refrigerant flow (VRF) system is a common air-conditioning system applied widely in residential and office buildings in China. Understanding the actual operation and performance of VRF systems is fundamental for the energy-efficient design and operation of VRF systems. Previous research on VRF system operation used either limited field data covering certain building types and climate zones or used a questionnaire to obtain a larger dataset. However, they did not capture the wide applications of VRF systems quantitatively across all building types, climate zones, and operating conditions. To fill this gap, statistical and clustering analysis was conducted on the newly proposed key performance indicators of approximately 287,000 VRF systems for residential and commercial buildings in all five climate zones in China. The main findings are: (1) VRF systems are mainly used for cooling in all climate zones in China; (2) among all building types, the duration of use is lowest in residential buildings and highest in hotels and medical buildings; (3) the distribution of the ideal VRF cooling coefficient of performance (COP) is similar across all climate zones and building types; whereas the COPs of ideal VRF heating in the Severe Cold region and Cold regions are lower than those in other climate zones; and (4) partial load operations for VRF systems are common in residential buildings and office buildings due to the part-time-part-space operation mode. These findings can inform the actual application of VRF systems in China, supporting the design, operation, industry standard development, and performance optimization of VRF systems.

An uncertainty-aware deep reinforcement learning framework for residential air conditioning energy management

Most existing methods for controlling the energy consumption of air conditioning (AC), focus on either scheduling the switching (on/off) of compressors or optimizing the overall energy consumption of AC system of an entire building. Unlike commercial buildings, residential apartments typically house separate ACs in individual rooms occupied by people with different thermal comfort preferences. Fortunately, the advancement of Internet-of-Things (IoT) technology has enabled the exploitation of sensory data to intelligently control the set-point temperature of ACs in individual rooms based on environmental conditions and occupant’s preferences, improving the energy efficiency of residential buildings. Indeed, control decisions based on sensory data may suffer from uncertainties due to error in data measurement and contribute to model uncertainty. This work proposes a data-driven uncertainty-aware approach to control split-type inverter ACs of residential buildings. First, information from similar AC and residential units are aggregated to reduce data imbalances, and Bayesian-Convolutional-Neural-Networks (BCNNs) are utilized to model the performance and uncertainty of the ACs from the aggregated data. Second, a Q-learning based reinforcement learning algorithm for set-point decision making is designed for setpoint optimization with transitions sampled from the BCNN models. Third, a case study is simulated based on such a framework to show that the control actions taken by the uncertainty-aware agent perform better in terms of discomfort management and energy savings compared to the uncertainty unaware agent. Further, the agent could also be adjusted to capture the trade-off between energy savings and comfort levels for varying degrees of energy and discomfort savings.

Study on the application of reinforcement learning in the operation optimization of HVAC system

Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation, while reinforcement learning (RL) is considered as a promising model-free supervisory control method. In this paper, we apply RL algorithm to the operation optimization of air-conditioning (AC) system and propose an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithm as well as complete application process. We use a variable air volume (VAV) air-conditioning system for a single-storey office building as a case study to validate the optimization performance of the RL-based controller. We select control strategies with the rule-based control controller (RBC) and proportional-integral-derivative (PID) controller respectively as the reference cases. The results show that, for the air supply of single zone, the RL controller performs the best in terms of both non-comfortable time and energy costs of AC system after one-year exploration learning. The total energy consumption of AC system reduced by 7.7% and 4.7%, respectively compared with RBC and PID strategies. For the air supply of multi-zone, the performance of RL controller begins to outperform the reference strategies after two-year exploration learning and two-year buffer stage. From the seventh year on, RL controller performs much better in terms of both non-comfortable time and operating costs of AC system, while the operating cost of AC system is reduced by 2.7% to 4.6% compared with the reference strategies. In addition, RL controller is more suitable for small-scale operation optimization problems.

Performance of a temperature and humidity independent control air-conditioning system based on liquid desiccant for industrial environments

Saving energy consumption of air-conditioning systems is becoming increasingly important in industrial buildings. In this study, measurements were conducted to reveal on-site performance of a full fresh air-conditioning system based on liquid desiccant applied in an industrial factory. Operating the system, temperature and humidity independent control (THIC) can be realized. The results reveal that the THIC system requires higher-temperature chilled water and no reheating process because of the adoption of a liquid desiccant dehumidifier, compared with the conventional vapor compression air-conditioning system. Therefore, a competitive system's coefficient of performance of 3.6 can be realized under the THIC system and an annual electricity-saving ratio of 23.3% can be achieved. In this system, the process air is divided into two parts, and the flow rate ratio of these two parts is discussed to further improve the system's performance. An optimal range of the ratio is determined in this study. When adjusting the ratio from the current 0.72 to 0.5, annual electricity consumption of the THIC system can be further saved by 5.4%. This study would provide valuable experience and reference for energy conservation of industrial air-conditioning systems.

The Calculation Method of Energy Consumption of Air-Conditioning System in Subway Vehicle Based on Representative Operating Points

In order to study the standard energy consumption of air-conditioning system of subway vehicle and optimize the performance of air-conditioning system, it is necessary to clarify the air-conditioning representative operating conditions. In this paper, based on the tested data during real operation of subway vehicle in summer, the characteristics of the air-conditioning operating conditions are analyzed, and the construction method of the operating conditions is first proposed. Secondly, taking the tested line as an example, using the constructed operating conditions as sample data, the k-means clustering algorithm is used to identify the representative operating points of the air-conditioning in the cooling season. Finally, the energy consumption based on the representative operating points of the cooling season is calculated by LMS AMEsim software, the error between calculated results and field measurements data is within 10%, which verifies the accuracy of energy consumption calculation method based on these representative operating points.

A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System

The energy consumption of air-conditioning systems is a major part of energy consumption in buildings. Optimal control strategies have been increasingly developed in building heating, ventilation, and air-conditioning (HVAC) systems. In this paper, a load forecast fuzzy (LFF) control strategy was proposed. The predictive load based on the SVM method was used as the input parameter of the fuzzy controller to perform feedforward fuzzy control on the HVAC system. This control method was considered as an effective way to reduce energy consumption while ensuring indoor comfort, which can solve the problem of hysteresis and inaccuracy in building HVAC systems by controlling the HVAC system in advance. The case study was conducted on a ground source heat pump system in Tianjin University to validate the proposed control strategy. In addition, the advantages of the LFF control strategy were verified by comparing with two feedback control strategies, which are the supply water temperature (SWT) control strategy and the room temperature fuzzy (RTF) control strategy. Results show that the proposed LFF control strategy is capable not only to ensure the minimum indoor temperature fluctuations but also decrease the total energy consumption.