Performance Of Air Conditioner Research Articles

Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand

Occupancy behaviour in buildings can impact the energy performance and operation of heating, ventilation and air-conditioning (HVAC) systems. HVAC, which uses conventional control strategies or “fixed” setpoint schedules, could not adjust to the conditioned spaces' actual requirements, resulting in building spaces being over or under-conditioned. While the unintended opening of windows can lead to substantial heat loss and consequently raises energy consumption. To optimise building operations, it is necessary to employ solutions such as demand-driven controls, which can monitor the utilisation of indoor spaces and provide the actual thermal comfort requirements of occupants. This study presents a novel vision-based deep learning framework for occupancy activity detection and recognition including the manual window operations in buildings. A region-based Convolutional Neural Network (R-CNN) model was trained and deployed to a camera for real-time detection and recognition. Based on the field experiments conducted within a case study University building, overall accuracy of 85.63% was achieved for occupancy activity detection and 92.20% for window operation detection. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable HVAC systems to respond to dynamic changes within indoor spaces. Results showed that the proposed approach could reduce the over-or under-estimation of occupancy heat gains compared with the use of “fixed” or static profiles. In addition, the approach can help alert building users or managers about windows left open unintentionally, which can reduce unnecessary ventilation heat losses. Furthermore, the approach can also predict the room CO2 concentration and advise occupants about a suitable natural ventilation strategy. The study highlighted the potential of the multi-purpose detection approach, but further development is necessary, including optimisation of the deep learning model, full integration with HVAC controls and further model training and field testing.

Coloured low-emissivity films for building envelopes for year-round energy savings

Buildings are responsible for over 40% of total US energy use, of which about 40% is directly related to the operation of heating, ventilation and air-conditioning (HVAC) systems. Saving energy to heat and cool buildings would contribute substantially to sustainability. Here we propose a category of coloured low-emissivity films for building walls that constitute the main component of the building thermal envelope. We demonstrate high reflectance (~90%) in infrared wavelength range and selective reflectance in the visible light wavelength range for desired colours. These films can help minimize radiative heat exchange between the indoor and outdoor environments, thus saving energy for all-year cooling and heating while satisfying the required aesthetical effect. Simulations show that these films can help reduce heat gain and loss by up to 257.6 MJ per installation wall area annually. In the case of a typical midrise apartment building, the HVAC system can save up to 124.46 GJ (equal to 9.87% of the building’s HVAC energy consumption). By rough estimation, a global CO2 emission reduction of 1.14 billion metric tons annually could be achieved. Our work provides insights for innovative energy-saving building envelope materials that can help achieve global carbon neutrality and sustainability.

Improving the Performance of Split Air Conditioner by Adding Nano Silver to the Compressor Oil

A high demand on the energy and power has brought scholars more attention to investigate new ways to reduce the energy consumption. The aim of this research is reducing the power consumption required for air conditioning to cool the room by adding nanomaterials to the compressor oil at a mass concentration of 0.15%. Two models of chambers with dimensions (2 x 2 x 2) meters were fabricated to represent the test section and connected to a 1ton compressor, one operating with natural oil as a standard basis for comparison, while the other one with Nano-oil prepared in the laboratory. The mass concentrations of 0.15% of silver nanoparticles (Ag) were prepared, and the stability of the nanoparticles was tested by direct observation for different time periods. The results indicated that the performance of the air conditioner was better with the addition of nanoparticles to the compressor oil compared to the pure oil in the compressor. Reducing energy consumption in the cooling system by (19%). Also, it was found that there is an increase in the cooling speed of the nanotechnology system by (6%)as well as in the efficiency of the Nano-oil system compared to the regular system by (13%). This study showed that the thermophysical properties of the cooling oil increased by increasing the weight ratios of solid nanoparticles by (12%).

AI-powered virtual assistants nudging occupants for energy saving: proactive smart speakers for HVAC control

ABSTRACT Virtual assistants powered by Artificial Intelligence (AI) and integrated into the smart home ecosystems facilitate human–building interactions. We have envisioned that the proactive virtual assistant capabilities could be designed to encourage energy conservation behaviours by relying on their nudging effect through conversational interactions, autonomous actuation and guiding users’ decision-making. To this end, we investigated how proactive virtual assistants, in a simulated smart home ecosystem, influence occupants to take energy-saving, adaptive actions for Heating, Ventilation and Air Conditioning (HVAC) operations and how participants’ personal characteristics affect their responses. Through an interactive online experiment, we collected data from 307 participants from diverse backgrounds across the United States. It was found that proactive communications with follow-up conversations can significantly increase the likelihood of accepting virtual assistance recommendations. This improvement was reflected in an increased number of participants (by 16%) who accepted energy-saving suggestions by comparing initial versus final responses during proactive conversations. Characterizing groups of participants based on their personal features and individual differences showed that user experience (with ∼30% increase), pro-environmental values/beliefs (with ∼24% to 35% increase) and forgiving thermal preferences (with ∼12% increase) had a significant influence on participants’ stated likelihood to accept virtual assistants’ recommendations and their evaluation of the general concept of proactive communication from virtual assistants.

Predictive modelling and optimization of HVAC systems using neural network and particle swarm optimization algorithm

The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

Influence of the position of recessed outdoor units and louvre blade angle on the performance of split air conditioners

In residential air conditioning systems, outdoor units are often installed in the recesses of building facades and shaded by louvres; however, different unit installation positions and louvre blade angles affect the thermal environment around the outdoor unit and the energy efficiency ratio (EER) of the air conditioner. In this study, the effects of the outdoor unit installation position and louvre blade angle on the EER when a single outdoor unit was installed in a recess were investigated by experiments on a 1.5 hp air conditioner (rated power of the air conditioner is 3.5 kW), and the influence of the spacing and angle between two outdoor units on the air conditioner EER when two outdoor units were installed in the same recess was explored. The results of the research indicate that when a single outdoor unit is installed in the recess, the EER increases with an increase in the distance between the inlet of the outdoor unit and the wall. To meet the three-level standard of air conditioner EERs, the distance between the inlet and wall needs to be greater than 300 mm. The EER first increased and then decreased slowly with the increase in the distance between the outdoor unit outlet and louvre; thus, the distance between the outlet and louvre should not be less than 300 mm. The EER first increased and then decreased with the increase in the blade angle, and thus, the blade angle should not be greater than 20°. When two outdoor units are installed in the same recess, each installation mode, “horizontal installation” (same height and collinear), “perpendicular installation” (same height and perpendicular), “angle installation” (same height and obtuse angle), and “up and down parallel installation” (different heights and parallel), has an optimum installation distance and angle.

Numerical Study on Coupled Operation of Stratified Air Distribution System and Natural Ventilation under Multi-Variable Factors in Large Space Buildings

Stratified air distribution systems are commonly used in large space buildings. The research on the airflow organization of stratified air conditioners is deficient in terms of the analysis of multivariable factors. Moreover, studies on the coupled operation of stratified air conditioners and natural ventilation are few. In this paper, taking a Shanghai Airport Terminal departure hall for the study, air distribution and thermal comfort of the cross-section at a height of 1.6 m are simulated and compared under different working conditions, and the effect of natural ventilation coupling operation is studied. The results show that the air distribution is the most uniform and the thermal comfort is the best (predicted mean vote is 0.428, predicted percentage of dissatisfaction is 15.2%) when the working conditions are 5.9% air supply speed, 11 °C cooling temperature difference and 0° air supply angle. With the coupled operation of natural ventilation, the thermal comfort can be improved from Grade II to Grade I.

Optimal Design of Battery Swapping-Based Electrified Public Bus Transit Systems

This article proposes a novel model to optimize the design of fully electrified public bus transit (PBT) systems that are operating using the concept of battery swapping. The proposed optimization model is formulated as a multiobjective mixed-integer nonlinear programming model that aims at minimizing the overall capital and operation expenditures of the electrified bus transit system. The formulated model determines the optimal configuration design parameters of the electrified bus transit system, including capacity of the onboard electric bus batteries, rated power of chargers, and the number of installed chargers and battery modules at the battery swapping station (BSS). Also, the model yields the optimal schedules of batteries swapping and charging scheme of batteries at the BSS. The model takes into consideration several physical and operation constraints, such as the limits of batteries state of charge, dynamic changes in the electricity prices, impacts of traffic conditions and heat, ventilation, and air conditioning (HVAC) operation on the battery electric bus (BEB) energy consumption, stiff and flexible schedule of bus assignments, and impacts of bus transit rush hour periods on batteries swapping. Several case studies are carried out on a real PBT system in the province of Ontario, Canada, to validate the effectiveness of the proposed model. The proposed model could be utilized as a decision-making tool to investigate the applicability of using the battery swapping concept to electrify bus transits based on the operation requirements and preferences of their operators.

Cooling seasonal performance of inverter air conditioner using model prediction control for demand response

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.

Performance improvement of CO2 air conditioner by integrating photonic radiative cooler as sub-cooler or/and roof envelope

Passive radiative cooling, which creates a direct in-situ cooling effect, along with a natural refrigerant-based vapor compression system, could be a potential alternative for building air conditioning to reduce the heat island effect and global warming. Hence, the radiative cooler is integrated with transcritical CO2 air conditioner as a sub-cooler or/and roof envelope. Three configurations of radiative cooler integrated CO2 system (Case 1: radiative cooler as roof envelope only, Case 2: radiative cooler as sub-cooler only and Case 3: radiative cooler as sandwiched roof envelope and sub-cooler) are proposed and simulated for tropical Indian summer climate. Energy, exergy and economic analyses along with gas cooler pressure optimization are performed. The Monte Carlo method is used to calculate annual energy savings for all three cases. Results show that case 3 exceeds the performance of other cases. Case 3 also reduces the variations of optimum gas cooler pressure and cooling load, leading to the smooth operation of the CO2 air conditioner. COP increment of 33.8% and exergy efficiency improvement of 29.1% are observed for case 3. A simple payback period of 5.3 years is worked out for case 3. Annual energy saving per unit area for case 3 is found comparable to solar photovoltaic integration.

Examination of Efficient Operation Method of ATES System by Comparison Operation with WTES System of Existent Heat Storage System

Aquifer thermal energy storage (ATES) system is widely used mainly in Europe and USA. In this paper, we examined the efficient operation method of ATES by comparing it with the water thermal energy storage (WTES) system of an existent thermal energy storage (TES) system using simulation. This study uses three aquifers: pumping wells, thermal storage wells, and reducing wells. The initial temperature is 19.1 °C groundwater from the surrounding area. ATES systems use the same operating methods as WTES systems to reduce heat storage efficiency and increase energy consumption. The operation that combines the ATES system with the pre-cooling/pre-heating coil can be used for air conditioning operation even if the heat storage diffuses or the pumping temperature changes. The aquifer heat storage system was used for the pre-cooling/pre-heating coil, and the cooling power consumption was reduced by 20%. The heating operation could not maintain heat for a long time due to the influence of groundwater flowing in from the surroundings. Therefore, it is recommended to use the stored heat as soon as possible. When energy saving is important by introducing a pre-cooling/pre-heating coil, the operation is performed by storing heat at a low temperature close to geothermal heat and also using groundwater heat. In addition, if the reduction of peak power in the daytime is important, it is appropriate to operate so that the heat stored in the pre-cooling/pre-heating coil is used up as much as possible. As a result, it was found that it is effective to operate the ATES system in combination with a pre-cooling/pre-heating coil. In cooling operation, ATES-C1-7 was the lowest at coefficient of performance (COP) 2.4 and ATES-C2-14 was the highest at COP 3.7. In heating operation, ATES-H1-45 was the lowest at COP1.2, and in other cases, it was about the same at COP2.4-2.8. In terms of energy efficiency, the heating operation ATES-H1-45 had a low energy efficiency of 4.1 for energy efficiency ratio (EER) and 3.9 for seasonal energy efficiency ratio (SEER). In other cases, the energy efficiency was 8.2–12.4 for EER and 8.7–15.3 for SEER.

Location combination optimization of thermal insulation material and phase-change material in multi-layer walls under air-conditioning continuous and intermittent operation

The multi-layer wall often employs the Thermal Insulation Material (TIM) or Phase-Change Material (PCM) layer to improve its thermal performance. To gain the location groups of the TIM and PCM layers, four wall models are built with air-conditioning continuous and intermittent operation, while temperature and heat flow in inner surfaces are used to evaluate the wall thermal performance by employing the numerical model with phase-change thermal transfer process. The result shows the air-conditioning operation mode has the large influence on optimizing the relative location of the TIM and PCM layers. Locating the PCM layer in the wall inside is the better choice with the higher applicability on outdoor thermal environment under air-conditioning continuous operation, while locating the TIM layer in the wall inside is the better one with the higher energy-saving contribution under air-conditioning intermittent operation. Air-conditioning intermittent operation can save 46.69–64.73% of the energy consumption but increase the peak load of urban electricity system, compared to air-conditioning continuous operation. However, for the multi-layer wall with the TIM layer in the wall inside, this negative effect is too small to be valued, compared to the higher energy-saving contribution.

A Machine Learning Approach to Predict the Performance of Refrigerator and Air Conditioning Using Gaussian Process Regression and Support Vector Methods

Aim: Develop machine learning models for the performance of refrigerator and airconditioning system. Background: The Coefficient Of Performance (COP) of Refrigerator and Air-Conditioning (RAC) is a complex function of evaporative temperature and concentration of nano-particle in lubricants. In recent years, researchers focus on experimental study for improvement of COP. Further, few researchers applied simulation techniques such as fuzzy system, Artificial Neural Network (ANN), simulated annealing, etc. to the Vapour Compression Refrigeration (VCR) cycle. There is a scarcity of modeling research work for the performance of RAC system. Objective: The study aims to develop the machine learning predictive models for the performance of refrigerator and air-conditioning system using experimental data. Methods: The experiment was performed on VCR system to determine COP. Three different concentration of lubricants (added 0.5, 1.0 and 1.5g nano-TiO2 particle on 1 liter of Polyolester (POE) oil) were used. The experimentally calculated COP was used to train and test the machine learning models. Gaussian Process Regression (GPR) and Support Vector Regression (SVR) methods were applied to develop the models. Results: The experimental result reveals that the COP increases with increasing the concentration (of nano particles) at a given temperature. The addition of 0.5 and 1.0g TiO2 in the POE oil shows better rate of increment in the COP in comparison to addition of 1.5g TiO2 in the POE oil. Machine learning models using GPR and SVR with RBF kernel function is the most appropriate machine learning model for the nonlinear relationship between the output parameter (COP) and the input parameter (evaporative temperature and concentration of TiO2). Conclusion: The present study was conducted to investigate the machine learning approaches for performance of RAC system using experimental data sets. The experimental result shows that R134a and TiO2-POE nanolubricant work efficiently and the coefficient of performance of VCR system increases with concentration of nano-particle. The developed model performance is compared using coefficient of correlation and RSME values. After comparison, it is concluded that RBF based GPR model is the best fit machine learning model to predict the COP in the context of any other model for this data set.

Advanced thermal environment and energy consumption calculation over time using combined energy simulation, computational fluid dynamics, and heat source model

We developed an unsteady computational simulation tool that sequentially inputs the calculated heat load to a heat source model to determine the coefficient of performance (COP) and power consumption of air conditioners. The accuracy of the energy consumption prediction was improved by incorporating the air-conditioning model into the coupled calculation of energy simulation and computational fluid dynamics as well as reproducing the behaviour of equipment efficiency. We conducted a comparative study in an office with a partition in the centre to investigate the effects of short circuits and heat accumulation. The experiments indicated a difference between the bulk temperature of the space and the input air humidity of the air conditioner of ∼2.0°C and 2.5 g/kg (DA), respectively. The coupled analysis confirmed that the power consumption of the bulk temperature/humidity input was ∼100 W and the COP error was ∼2, while the suction temperature/humidity input had high accuracy.

Experimental study on an air-cooled air conditioning unit with spray evaporative cooling system

Improving the coefficient of performance (COP) of air conditioner with air-cooled condenser is a challenge especially in areas with hot weather. A spray evaporative cooling system is coupled with the air-cooled condenser of an air conditioning unit, and influence of different outdoor temperatures, mass flow rates of nozzles and operation modes of spray system on the performance of air conditioning system under are investigated. Experimental results show that the application of spray evaporative cooling system has significant effect on the performance improvement of the unit and the improvement rate rises as outdoor temperature increases. By using the spray evaporative cooling system in hot weather conditions, the power consumption can be reduced up by 22% and the COP can be improved by around 42.6%. Even if the spray energy consumption is taken into account, the values of COP are higher than the threshold value of the secondary energy efficiency (3.2). This study demonstrates that an air-cooled air conditioning unit coupled with spray cooling system is a kind of energy-efficient technique, which has a significant potential to improve the unit performance and reduce the electricity demand.

에어컨 풍속 저감에 따른 실내 비말 확산 영향 : 실험실 입자 방출 실험을 통한 입자농도 분석

Purpose: The COVID-19 virus has spread rapidly around the world. Recently, many cases of group infection have been reported due to the operation of indoor air conditioners (AC). That is because the use of AC promotes the spread of droplets by increasing the indoor airflow velocity. Therefore, this study aims to analyze the droplet diffusion according to the airflow velocity of the AC. Method: Particle generation experiments were conducted in the laboratory. The following two cases were compared. Case 1: Conventional AC, Case 2: Low velocity AC (<0.15m/s) Result: The experimental result showed that the maximum concentration of PM10 was about 30% lower in Case 2 than in Case 1 during droplet dispersion. In the case of droplets with a large particle size (>5μm), which are highly likely to spread the virus, the maximum concentration of Case 2 was 67% lower than that of Case1. In the comparison of the loss rate, Case 2 showed a higher reduction rate of about 22% to 29% than that of Case 1. As a result, it was confirmed that wind velocity has a large influence on indoor droplet dispersion. Therefore, In summer, when air conditioners are frequently used, it is more effective to use a low velocity AC to prevent the spread of corona virus.

Performance of Air Conditioning Unit under Constant Outdoor Wet-Bulb Temperature and Varied Dry-Bulb Temperature

An experiment has been carried out for examining the performance of an air conditioning unit under constant outdoor wet-bulb temperature and varied dry-bulb temperature. During the experiment, the wet-bulb temperature of the compartment for outdoor unit was maintained at 22℃ and the dry-bulb temperature was varied from 24℃ to 36℃. The increase of outdoor air temperature results in the increase of supply air temperature, discharge temperature, suction temperature, and liquid line temperature. These cause the degradation of the air conditioner performance. An increase of power consumption by 1.4% and decrease of cooling capacity by 0.8% were observed for each 1℃ increase of outdoor air temperature. As a result, the energy efficiency ratio drops by 2% for each 1℃ increase of outdoor air temperature.

Effect of Different HVAC Control Strategies on Thermal Comfort and Adaptive Behavior in High-Rise Apartments

In the residential sector, householders play an active role in regulating the indoor climate via diverse control measures such as the operation of air-conditioners or windows. The main research question asked in this paper is whether control decisions made by householders are rational and effective in terms of achieving comfort and energy efficiency. Based on a field study in South Korea, this paper explores how a HVAC control strategy for high-rise apartment buildings can affect occupant comfort and adaptive behavior. Two different control strategies: (1) occupant control (OC), where occupants were allowed to freely operate the HVAC system and (2) comfort-zone control (CC), where the operation of the HVAC system was determined by the researcher, based on a pre-defined comfort zone, were applied to, and tested within the participating households in summer. The impact of the two control strategies on indoor thermal environments, thermal comfort, and occupant adaptive behavior were analyzed. We find that the CC strategy is more energy/comfort efficient than OC because: (1) comfort was be achieved at a higher indoor temperature, and (2) unnecessary control behaviors leading to cooling load increase can be minimized, which have major implications for energy consumption reduction in the residential sector.

Automobile Absorption Conditioner

The purpose of this study is to develop a circuit for an automobile air conditioner, which will reduce the consumption of power developed by the engine. This paper proposes the design of an automobile absorption air conditioner. A description of the principle of operation of an automobile absorption air conditioner operating on a cycle of a one-stage absorption refrigeration machine has been given in the paper. It consists of a stripper (generator), a condenser, an absorber, an evaporator. Lithium bromide (LiBr) solution has been used as an absorbent, which has a low boiling point, is non-toxic and safe. 3D-models of the absorber and generator of an automobile absorption air conditioner has been developed in the course of the research. The absorber is designed to form a weak absorbent solution. This solution is supplied to the generator heat exchanger using a liquid pump. There it is heated by the exhaust gases to the boiling point. The solution evaporates and water vapor enters the condenser (evaporator). In the generator, the solution is concentrated from 52 to 60 %. After that, water vapor is supplied to the absorber from the condenser, and a concentrated absorbent solution is supplied from the generator. It should be noted that the generator is a key element of an automobile absorption air conditioning system. Inside it is a strong LiBr solution that feeds the absorber. The design of the air conditioning system does not provide for the use of a compressor and allows to reduce the power loss of the power plant to the drive of the liquid pump. According to calculations, the pump drive power was 0.17 kW. For comparison, the compressor of a modern car air conditioner consumes 7–11 kW. An absorption car air conditioner provides the following advantages: additional engine cooling, environmental friendliness, fuel economy, efficient use of the heat of vehicle exhaust gases. A distinctive feature of this design is that it is proposed to use the heat of the exhaust gases for the process of heating the absorbent. This design can fully compete with the existing modern car air conditioners.

Energy-saving Renovation of Air Conditioning in Technical Schools Based on the Internet of Things

In public places such as teaching buildings and dormitories, a single air conditioner does not seem to consume much energy, but the entire school consumes a lot of energy. Designing a series of air-conditioning energy-saving systems that can be automatically controlled to protect students’ body temperature while achieving energy saving and reducing energy consumption is an important research direction at present. The research purpose of this article is based on the analysis and research of energy-saving renovation of air conditioning in the Internet of Things technical school. The main purpose of this paper is to further realize the remote monitoring of campus air-conditioning system in our country. Taking our campus air-conditioning system as the main research object, we will develop a new campus air-conditioning monitoring system composed of air-conditioning monitoring terminal, monitoring terminal and air-conditioning control center. The monitoring department uses wireless communication technology to solve the energy consumption problem of traditional campus air conditioners. At the same time, it also establishes a network of remote sensors and remote monitoring, so that the monitoring and control methods of remote sensors during the operation of air conditioners are not affected by any region. According to experimental data, the Internet of Things technology effectively reduces the energy consumption of air conditioners by 30%, and indirectly reduces manpower consumption.