Building Air Conditioning Systems Research Articles

Utilisation of Machine Learning in Control Systems Based on the Preference of Office Users

Reducing energy consumption is vital to save natural resources and contribute to the sustainable development in any sector of society. In the building sector, there are many well-known energy efficiency strategies currently being applied. However, considering the advances in technology and in comfort studies, it is possible to see that the current building sector scenario demands new energy efficiency strategies. Such strategies need to be capable of identifying and assuring comfortable environments according to users’ perceptions. Machine learning techniques can be a useful alternative to identify users’ preferences and control lighting and heating, ventilation and air-conditioning systems in buildings. This paper shows a systematic literature review on the use of machine learning algorithms on preference identification and environmental adequacy according to users’ demands. Its contribution is to explore beyond the performance and configurations of the algorithms, addressing users’ preference aspects as well. The strategies found in the literature provided promising results. The most used approach was supervised learning because data can be treated as categories. In general, the control systems have shown good performance, and so have the algorithms. Users were mostly satisfied with environmental conditions. Situations of dissatisfaction were associated with the occupant’s willingness to use the system more than with the control system’s performance. Furthermore, it is also possible to ally user-centred control and energy savings but this relies on occupants’ characteristics and the control strategies used. We underline the importance of identifying whether the users are willing to deal with an automatic control system before making any decision, even if the operation of the system is based on their preferred environmental conditions.

Unveiling overlooked aspects of model predictive control for building air conditioning systems

The ongoing research on model predictive control (MPC) for building air conditioning systems predominantly centers on improving the predictive capabilities of system models. In this paper, the impacts of three additional pivotal factors on MPC performance are assessed by examining a generic MPC design for a typical variable air volume (VAV) system that serves large commercial buildings. The three factors encompass the nuanced reformulation of optimization, the judicious relaxation of constraints, and the meticulous tuning of parameters. Detailed case studies with an integrated Modelica and EnergyPlus model of the US Department of Energy's Commercial Reference Building are conducted. The results confirm that the optimization formulation, along with relaxation methods, significantly affects MPC performance in terms of energy savings, zonal thermal comfort level, and computational demand. They also reveal that the impact of the MPC control parameters on the energy savings and thermal comfort may vary by season and can be non-monotonic.

Advancing energy efficiency in livestock building: Simplified building energy simulation tool for geometric design of pigsty

Concerns related to food security and animal welfare have led to increasing interest in analyzing the energy demands of heating, ventilation, and air conditioning (HVAC) systems in livestock buildings. However, building energy simulation (BES) model, which facilitates precise HVAC systems energy analysis, is difficult to use for non-experts, and particularly, its usability drops significantly in livestock buildings, which are often constructed without involvement of building engineers. This study, therefore, developed a tool that can analyze the energy demand of pigsties by changing geometric design variables with only several numerical inputs without separate 3D modeling. Pigsty geometric design types were classified by examining pigsty architectural design manual and web portal map search. A simplified tool, utilizing EnergyPlus and MATLAB graphical user interface, was developed to analyze energy demand in pigsties by leveraging simple user inputs for geometric design modeling. This tool simplifies exploring potential energy savings through geometric design changes in the early design stages of livestock buildings. The methodology introduced in this study lays the groundwork for future enhancements, including the integration of various design-phase variables beyond geometric design.

Reconfigurable supply-based feedback control for enhanced energy flexibility of air-conditioning systems facilitating grid-interactive buildings

Air-conditioning systems have great potential to provide energy flexibility services to the power grids of high-renewable penetration, due to their high power consumption and inherent energy flexibilities. Direct load control by switching off some operating chillers is the simplest and effective means for air-conditioning systems in buildings to respond to urgent power reduction requests of power grids. However, the implementation of this approach in today's buildings, which widely adopt demand-based feedback controls, would result in serious problems including disordered cooling distribution and likely extra energy consumption. This study, therefore, proposes a reconfigurable control strategy to address these problems. This strategy consists of supply-based feedback control, incorporated with the conventional demand-based feedback control, a control loop reconfiguration scheme and a setpoint reset scheme, facilitating effective control under limited cooling supply and smooth transition between supply-based and demand-based feedback control modes. The proposed control strategy is deployed in a commonly-used digital controller to conduct hardware-in-the-loop control tests on an air-conditioning system involving six AHUs. Test results show that the reconfigurable control achieves commendable control performance. Proper chilled water distribution enables even thermal comfort control among the building zones during demand response and rebound periods. Temperature deviation of the building zones is controlled below 0.2 K most of the time. 11.6 % and 27 % of power demand reductions are achieved during demand response and rebound periods respectively, using the proposed reconfigurable control compared with that using conventional controls.

Parametric analysis for nanolubricant in vapor compression refrigeration cycle

With the escalating global energy crisis and the consequential surge in energy consumption, particularly attributed to air conditioning (AC) systems in buildings, there arises a pressing need for sustainable solutions to mitigate this escalating demand. Vapor compression refrigeration (VCR) cycles, ubiquitous across residential, commercial, and industrial sectors, represent a significant portion of global electricity consumption, exceeding 10%. This demand is further exacerbated in regions characterized by hot climates. Consequently, there is a critical imperative to address the environmental impact of these systems, particularly in terms of reducing their Global Warming Potential (GWP). In response to this imperative, this study aims to develop a comprehensive numerical model for VCR systems, focusing on the implementation of nanolubricants. The primary objective is to assess the impact of integrating Al2O3 nanoparticles into the lubricating oil (referred to as nanolubricant) on the performance of the VCR cycle. The scope of the study encompasses the investigation of nanolubricants within refrigeration systems, aiming to enhance their efficiency and environmental sustainability. Utilizing the Engineering Equation Solver (EES), a numerical model of a VCR cycle utilizing R-410A refrigerant is developed. The model specifically emphasizes the compressor component, which is flooded with a polyolester (POE) oil base. The study explores various parameters, including ambient conditions, refrigerant-to-oil dissolving ratio, and mass concentration of nanoparticles, to evaluate their influence on system performance. The results indicate a significant enhancement in performance when flooding the base oil before introducing Al2O3 nanoparticles, with further improvements observed upon nanoparticle integration. Specifically, the coefficient of performance (COP) of the VCR system exhibits a notable increase compared to the base case configuration. Additionally, the adoption of eco-friendly refrigerants, such as R-32, demonstrates promising COP values following nanoparticle implementation by up to 4%. Moreover, the study reveals a reduction in compressor discharge temperature by up to 20% with higher oil mass concentrations during nanolubricant implementation, indicating enhanced durability and longevity of the VCR system’s compressor. These findings underscore the potential of nanolubricants to contribute to both the efficiency and environmental sustainability of refrigeration systems. In conclusion, the findings of this study offer valuable insights into the utilization of nanolubricants in VCR cycles, highlighting their potential to enhance system performance and mitigate environmental impact. These advancements hold promise for the development of more sustainable refrigeration technologies in the face of mounting energy challenges.

Chiller fault diagnosis based on improved variational autoencoder and co-training framework: A case study of insufficient samples

Implementing efficient automatic fault diagnosis is critical for saving energy and minimizing financial losses in the heating ventilation air-conditioning (HVAC) systems of commercial buildings. However, the limited quantity and weak features of fault samples acquired during HVAC operations hinder the effectiveness of conventional machine learning-based fault diagnosis methodologies. This paper proposes a method based on an improved conditional variational autoencoder (MCVAE) and co-training (CT) ideology to address the issue of insufficient training samples. Initially, we employ MCVAE to synthesize an extensive dataset of chiller fault samples from the original training dataset. Subsequently, the beneficial samples for training our fault diagnostic classifier, namely high-quality samples, are selected from the generated dataset using the CT-based framework. Finally, the selected high-quality samples are merged into the original training dataset to train the ultimate fault classifiers. Experimental results demonstrate that our proposed method outperforms in effectiveness and efficiency compared to recently published methods. For instance, in the case of fault level 1 compared to the suboptimal model, our approach exhibits improvements of 2.41% when each type has 5 fault samples.

Energy efficiency of life support systems of buildings in «green construction»

Purpose: develop an integrated air conditioning system with the environment, which will allow to reduce energy consumption due to lowering the temperature of air taken from outside from natural or artificial green areas. Methods: the basis of the solution to the task of developing an integrated system was the use of three well-known methods of greening of home territories: traditional, non-traditional and container. Because green spaces, in the warm period of the year, allow you to naturally reduce the parameters of the outside air, which are used for the needs of the building's ventilation and air conditioning systems. Findings: due to the complex use of all types of landscaping, it is possible to reduce the energy consumption for work of ventilation and air conditioning systems by reducing the temperature of air taken from the outside from natural or artificial green areas. Due to this, when developing integrated air conditioning systems, we can achieve: a decrease in the temperature of the supply air from the landscaping area, which will reduce not only the energy costs for cooling and humidification, but also the reduction of pollution of the supply air; when using trees and shrubs of certain breeds, which emit phytoncides and other beneficial secretions, in the outdoor air intake area, it will improve the quality of incoming air. Practical implication: the developed integrated air conditioning system with the environment will allow to reduce the influence of the outside air temperature on the indoor air temperature due to the integrated use of all types of greening. Originality: a review of literary sources showed that today the possibility of taking outside air from existing or artificially created green areas is not used in the design of ventilation and air conditioning systems.

Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence

The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) methods have been developed for building air conditioning systems, they are difficult to be applied to bus air conditioners since its operation is highly dynamic and fault-free data are usually unavailable. Therefore, this paper proposes an FDD method for electric bus air conditioners to tackle the above issues. First, the method identifies faults in an unsupervised manner by comparing selected features among a group of peer systems. Then, considering the features are influenced by the operating conditions, Gaussian process regression (GPR) models are established to find the relationships between each feature and its influential parameters. The probabilistic nature of the GPR is used to differentiate predictions with large uncertainty, which are then excluded from FDD. In this way, robustness of the method is evidently improved. Finally, fault indexes are defined to detect and diagnose mechanical faults. We applied the method to a group of air conditioners in a city bus fleet. Results showed that it can effectively identify refrigerant undercharge and indoor and outdoor fan problems with low false positive/genitive rates. Also, the method is highly robust and not sensitive to the faulty systems in the bus fleet.

Grey-box model-based demand side management for rooftop PV and air conditioning systems in public buildings using PSO algorithm

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.

Low carbon operation optimisation strategies for heating, ventilation and air conditioning systems in office buildings

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in production environments. Optimising the control strategy of an HVAC system is an effective approach to achieving energy savings and reducing emissions in a production environment. In existing optimal control models for HVAC systems, occupant behaviour has been incorporated as a key influencing factor. However, model predictive control algorithms have not introduced to examine its performance in optimising HVAC system operation. An optimisation strategy considering occupant (leaders and users of HVAC systems) behaviour (OSOB) is proposed based on model predictive control algorithm. The concept of environmentally specific transformational leadership is introduced to characterise the behaviours of leaders. Then the distinct decisions driven by double objectives (carbon emissions and electricity costs) can be explained. The OSOB was applied to a representative office building in Beijing. The findings indicate that incorporating occupant behaviour into algorithm can lead to a significant reduction in carbon emissions, up to 45%, as well as a decrease in electricity bills, up to 17%. This study not only incorporates occupant behaviour as a significant influencing factor, but also offers valuable insights for product manufacturers seeking to reduce the carbon emissions produced during building operations.

Data and knowledge fusion-driven Bayesian networks for interpretable fault diagnosis of HVAC systems

Timely diagnosis and maintenance of faults in chiller units is beneficial for reducing energy consumption in buildings. In order to improve the diagnostic accuracy and provide reasonable explanations for fault occurrences of the heating, ventilation, and air conditioning (HVAC) systems in buildings, a fusion-driven Bayesian network fault diagnosis method is proposed based on broad learning system and fuzzy expert knowledge. Firstly, from the data-driven perspective, an efficient broad learning system is developed as benchmarking model to build the prior Bayesian network structure. Secondly, from the knowledge-driven perspective, the fuzzy logic system is used to fuzz expert knowledge, which then used to optimize the Bayesian network. Finally, the information obtained by experts on-site is incorporated into the optimized Bayesian network as new evidence nodes to determine the Bayesian network. The parameters of the Bayesian network are learned through Noisy-MAX processing of the conditional probability table. The performance of the proposed method is evaluated based on the fault diagnosis of a chiller system. The results demonstrate that the Bayesian network established through this method combines the advantages of data-driven and knowledge-driven approaches. It not only performs well in terms of diagnostic accuracy, with accuracy rates above 98 % for six typical faults, but also provides effective explanations for the underlying mechanisms of the faults through causal relationship diagrams.

Personal Comfort System Approach Associated with Fan Usage: A Review

Due to the significant energy consumption of the air conditioner (AC), an alternative energy-saving method is required. To achieve this objective, the design of a building's heating, ventilation, and air conditioning (HVAC) systems must comply to the energy consumption criteria without sacrificing occupant comfort. In this instance, the personal comfort system (PCS) provides a promising means of meeting the demand. This paper aims to summarize the recent discoveries and research on personal comfort systems combined with fan usage. The PCS in conjunction with fan use, which is regarded as the most prevalent thermal adaptation behaviour, offers advantages in terms of conditioning the indoor thermal environment, modulating the person's interaction with the surrounding environmental control system, and reducing the excessive use of energy on HVAC.

The impact of wettability on the performance of a two row wavy fin and tube liquid desiccant system for dehumidification and regeneration

This study investigates the challenge of enhancing wettability in falling film type dehumidifiers/regenerators, a crucial factor in optimizing the performance of building air conditioning systems. The emergence of Severe Acute Respiratory Syndrome (SARS) has underscored the need for improved indoor air quality (IAQ) and energy efficiency. This research specifically addresses the suboptimal wettability issue in fin and tube liquid desiccant systems, which is essential for efficient dehumidification and regeneration processes. The experimental setup employed a cross-flow configuration within the heat exchanger elements, where the flow of ionic liquid intersected with horizontal airflow through cooling and heating coils. By systematically adjusting the wettability on the heat exchanger surfaces, the study examined its impact on relative humidity and dry bulb temperature at the system's inlet and outlet. The results indicate that the wetting fraction on fin and tube surfaces significantly influences the delta humidity ratio of air. In the dehumidification phase, for instance, the delta air humidity ratios varied between 9.4 and 9.7 g/kg with corresponding wetting ratio values ranging from 0.47 to 0.5. Similarly, during regeneration, delta air humidity ratios fluctuated from 9.08 g/kg to 9.6 g/kg, with wetting fractions between 0.47 and 0.5. In conclusion, this study offers in-depth insights into the impact of wettability on the efficiency of fin and tube liquid desiccant systems. These findings have implications for enhancing IAQ and energy efficiency in building air conditioning, particularly in tropical regions like Indonesia.

Economic Model Predictive Control in Buildings Based on Piecewise Linear Approximation of Predicted Mean Vote Index

Energy shortage is a challenge for many countries, and building energy consumption accounts for a considerable proportion of global energy consumption. The main work of this paper is to optimize the energy consumption of heating, ventilating, and air conditioning (HVAC) systems in buildings based on economic model predictive control (EMPC). The cost in EMPC design includes energy consumption and predicted mean vote (PMV), which is an index that evaluates the thermal comfort of indoor occupants. In order to model the nonlinearity of the PMV index, we propose a lattice piecewise linear (PWL) approximation, which has high approximation precision and facilitates the resulting optimization problem, which is basically a piecewise quadratic programming problem. For the piecewise quadratic programming, we propose a descent algorithm that converges quickly and scales well with the length of the prediction horizon in the EMPC problem. The experimental results demonstrate that the proposed method saves 19.78% of the electricity cost compared to the conventional control strategy and significantly increases indoor comfort. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> — The motivation of this article is to provide a control strategy to reduce building energy consumption and ensure indoor thermal comfort. In most of the existing methods for air conditioning temperature control, the occupants’ comfort hasn’t been considered. In this paper, thermal comfort is described by the PMV index, which is basically nonlinear. In order to model the thermal comfort more accurately, in this paper, the PMV index is approximated piecewise linearly in order to meet the requirements of accuracy and computational efficiency. The resulting optimization problem is not hard to solve, and we provide an efficient algorithm for solving this optimization problem. Preliminary simulation experiments demonstrate that this approach is practical, i.e., it achieves energy reduction and ensures thermal comfort. Our strategy, however, has not yet been deployed in real buildings. In future research, we will propose similar techniques for large-scale systems in order to solve energy optimization problems containing multiple thermal zones and realize the proposed technique in real buildings.

Energy saving control method for central air conditioning systems in public buildings based on improved particle swarm optimisation

Energy saving control method for central air conditioning systems in public buildings based on improved particle swarm optimisation

Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM

Building load prediction is one of the important means of saving energy and reducing emissions, and accurate cold load prediction is conducive to the realization of online monitoring and the optimal control of building air conditioning systems. Therefore, a joint prediction model was proposed in this paper. Firstly, by combining the Pearson correlation coefficient (PCC) method with sensitivity analysis, the optimal combination of parameters that influence building cooling load (BCL) were obtained. Secondly, the parameters of the support vector machine (SVM) model were improved by using the genetic algorithm (GA), and a GA-SVM prediction model was proposed to perform building hourly cold load prediction. Then, when there is a demand for the fluctuation prediction of BCL or extreme weather conditions are encountered, the information granulation (IG) method is used to fuzzy granulate the data. At the same time, the fluctuation range of the BCL was obtained by combining the prediction of the established GA-SVM model. Finally, the model was validated with the actual operational data of a large public building in Xi’an. The results show that the CV-RMSE and MAPE of the GA-SVM model are reduced by 58.85% and 68.04%, respectively, compared with the SVM for the time-by-time BCL prediction, indicating that the optimization of the SVM by using the GA can effectively reduce the error of the prediction model. Compared with the other three widely used prediction models, the R2 of the GA-SVM model is improved by 4.75~6.35%, the MAPE is reduced by 68.00~72.76%, and the CV-RMSE is reduced by 59.69~64.97%. This proved that the GA-SVM has higher prediction accuracy. In addition, the joint model was used for BCL fluctuation range prediction, and the R2 of the prediction model was 97.27~99.68%, the MAPE was 2.59~2.84%, and the CV-RMSE was only 0.0249~0.0319, which demonstrated the effectiveness of the joint prediction model. The results of the study have important guiding significance for building load interval prediction, daily energy management and energy scheduling.

Multi-Objective Optimization Research on the Integration of Renewable Energy HVAC Systems Based on TRNSYS

Well-designed passive buildings can drastically reduce building energy consumption, and optimal design of air conditioning systems is the key to achieving low operating energy consumption in near-zero energy buildings. TRNSYS was used to build the simulation model for a near-zero-energy building and its air conditioning system in Beijing. The Taguchi method was used to sort the design parameters that affect system performance according to the degree of influence and find the best combination of design parameters to optimize the system, which increased the solar fraction of the system by 4.6% and reduced the annual operating energy consumption by 7.32%. For the optimized system, a multi-objective optimization function of the life cycle costs and carbon emissions was established. By comparing the energy consumption, life cycle costs, and carbon emissions of the air conditioning system under different system configurations, optimal configuration solutions under different design target weights were obtained. It was found that using a ground source heat pump system + solar collector system had better energy-savings benefits, but the operating costs were slightly higher. The application of absorption refrigeration can reduce the system operating costs but will increase the initial investment. The best economic benefits were achieved using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system for cooling in summer, and the best environmental benefits were obtaining using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system + solar absorption refrigeration system in summer, which provides a reference for the optimization design and research of air conditioning systems in near-zero energy buildings.

Flexibility potential quantification and regulation measure comparison for the building air-conditioning system

Air-conditioning (AC) systems are the resources with the greatest flexibility potential for building demand response (DR). However, there is no unified method to quantify its flexibility under different DR measures, which results in difficulties in fully utilizing the DR potential and determining a proper regulation measure for the AC system operation strategies. To bridge this gap, the indicators of energy flexibility and power flexibility are firstly proposed in this study to compare three measures, active storage, passive storage, and temperature resetting, during the DR process of AC systems. According to the regulation measures with quantified flexibility potentials, different DR regulation measures are combined to explore the maximized stack flexibility. Based on a case study of an office building in Tianjin, the results show that among the different stacking strategies, stacking the active energy storage and temperature reset strategies produces the lowest operating cost, which is 13.8% and 22.5% lower than the stacking all three measures’ operating cost and total energy consumption, respectively. In the case of stacking the two measures, the highest energy flexibility is achieved when stacking the active and passive energy storage measure. If the temperature reset measure is further stacked, the energy flexibility can be increased by 15%.

Influence of cross-ventilation cooling potential on thermal comfort in high-rise buildings in a hot and humid climate

The rising cooling energy demand, caused by the increase in global average temperature and the frequency of heat waves, is currently leading to a significant increase in global electricity consumption. Passive cooling strategies, such as ventilative cooling, can provide acceptable indoor thermal comfort with no or minimal energy consumption, offering an alternative to air conditioning systems in buildings. This paper investigates the effectiveness of natural ventilation in an iconic post-modernist high-rise building in India: Charles Correa's Kanchanjunga Apartments. The research, employing CFD simulation paired with building energy simulation, demonstrates how cross-ventilation can improve the ventilative cooling efficiency in buildings, ensuring affordable indoor thermal conditions even in adverse climatic conditions like the Indian one. Thanks to night-time structural cooling, a maximum reduction in the operative temperature of up to 5.3 °C was achieved compared to the scenario without natural ventilation and up to 6.4 °C compared to the outdoor temperature peak on the hottest day. Consistently with the Indian Model for Adaptive Comfort, cross-ventilation ensures a reduction in discomfort hours of up to 58 % on the hottest day and a total reduction in discomfort hours on a typical monsoon and winter day. The paper aims to assist architects and policymakers in quantifying the cooling potential of natural ventilation in high-rise buildings, suggesting passive solutions for cooling energy saving.

Design and Development of IoT-based Temperature and Humidity Monitoring System for Thermal Comfort Evaluation in Classrooms

In the learning process in the classroom thermal comfort greatly affects the attitude and performance of students who are doing learning. However, in fact, when students are carrying out the learning process experience some obstacles that make them not feel comfortable in the classroom such as feeling the room temperature is too hot or the room temperature is too cold. Indonesian National Standard sets the air temperature for thermal comfort of the tropics which can be divided into (1) Cool comfort, between the effective temperature of 20.5 ° C - 22.8 ° C, (2) Optimal comfort, between the effective temperature of 22.8° C - 25.8 ° C, (3). Comfortably warm, between an effective temperature of 25.8°C - 27.1°C. While the relative air humidity that is comfortable for humans ranges from 30%60%. The purpose of the temperature and humidity monitoring system is to monitor temperature and humidity conditions that can be used to determine whether the room is in a comfortable condition or not. In this study, the reference used is the 2001 Indonesian National Standard on Procedures for Designing Ventilation and Air Conditioning Systems in Buildings.