Air Conditioning Control System Research Articles

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Reinforcement learning (RL) has great potential in achieving energy-efficient, comfortable and intelligent control of heating, ventilation and air conditioning (HVAC) systems. Although research on RL-based HVAC control has attracted increasing interest, current studies generally use simple building simulation as the environment to train agents, and the definition of thermal comfort is limited to a wide temperature range, which cannot meet the different thermal comfort requirements of various occupants. This study proposes a deep reinforcement learning (DRL) control framework based on the Dueling Deep Q-network (DQN) algorithm, combined with a self-designed environmental model and reward function, for HVAC control meeting different thermal comfort requirements. Specifically, based on the theory of building thermal dynamics, a nonlinear equation modified by experimental data is used for the environmental model that reflects the actual thermal change of building. Different thermal comfort requirements are considered and analysed through a dynamic predicted mean vote (PMV) model that focuses on the metabolic rate and clothing level of occupants. By systematically exploring different heating modes for occupants and control time intervals, the proposed framework demonstrates that heating energy consumption can be reduced by 4.8%-39.58% under various conditions compared to rule-based control. In addition, the study found that the HVAC control based on DRL has greater potential in saving energy when the heating demand of building is higher. Our study is helpful for researchers to make HVAC control more energy-efficient and user-friendly with the help of artificial intelligence.

Development of an IoT-Based Air Conditioning Meter for Real-Time Operational Status

Air conditioning systems have become a staple in households and businesses, particularly in regions with tropical climates, where maintaining indoor comfort is crucial. However, the widespread use of air conditioning units has led to a significant increase in energy consumption, contributing to higher electricity bills and environmental concerns, particularly in terms of carbon emissions. Traditional air conditioning units often operate without real-time monitoring, leading to inefficiencies and unnecessary energy use. In response to these challenges, there has been a growing interest in the development of smart, energy-efficient solutions that can monitor and control air conditioning systems remotely. The advent of the Internet of Things (IoT) has opened up new possibilities for integrating sensors and microcontrollers with cloud-based platforms, enabling real-time data collection and analysis. This research builds on these advancements by developing an IoT-based meter designed to monitor the operational status of air conditioning units, providing users with immediate feedback and enabling more efficient energy management.

Application of energy combined thermal comfort in intelligent building management in complex environments

The efficient operation of heating ventilation and air conditioning systems relies on advanced control strategies. However, current control methods are often limited by issues such as uncertain system parameter information and spatial coupling constraints related to the supply rate of the air supply fan. To this end, an energy joint thermal comfort management method for complex environments in multiple regions is proposed. The long-term total cost minimization of the system is established, and then the Lyapunov optimization technology is used to design the distributed control algorithm. Simulation validation shows that the proposed method reduces the energy cost by an average of 11.24% compared to other methods with a thermal discomfort cost coefficient of 0. The average temperature deviation in the area is improved by 0.15 °C and 0.68 °C, respectively. The method saves more than 10% of the total energy cost under different thermal perturbations with an average total temperature deviation of 0.04 °C. The results indicate that the proposed energy joint thermal comfort management method can flexibly balance energy costs and user thermal comfort without knowing any prior information of system parameters, which can also greatly protect user privacy information. This method has application value in the control of heating ventilation and air conditioning systems in complex environments such as commercial buildings.

Automatic control of constant temperature and humidity in building air conditioning systems based on frequency domain analysis

How to solve their automatic control of constant temperature and humidity gradually becomes a research hotspot as the continuous upgrading of air conditioning systems. This study aims to optimize the traditional proportional-integral-differential controller for improvement to solve the time-delay instability phenomenon in temperature and humidity control. The objective of this study is to optimize existing proportional-integral-differential controllers to improve the time-delay instability problem that is common in temperature and humidity control. Firstly, it treats the controlled object as a first-order and second-order system with time-delay characteristics. Next, the Smith predictor controller is generalized equivalent to ensure that the equivalent system does not contain time-delay. Finally, an analysis of the first-order and second-order closed-loop control system is conducted by combining Smith predictive controller and proportional-integral-differential controller. The system achieves the goal of automatic control of constant temperature and humidity by adjusting the control parameters. The experiment showcased that the temperature control time of the proposed control scheme under first-order and second-order time-delays was 16 s and 3 s, respectively. Meanwhile, the humidity control time was 14 s and 13 s, respectively. In practical applications, the proposed control scheme achieved good control effects in all four seasons. This indicates that the controller designed in this study possesses good control performance. It also can achieve the goal of constant temperature and humidity control. This can provide technical support for the automation control of air conditioning systems.

Research and Design of Heat Pump Air Conditioning Control System Based on Intelligent Diagnosis

Research and Design of Heat Pump Air Conditioning Control System Based on Intelligent Diagnosis

Predicting individual thermal preferences in an office: Assessing the performance of mixed-effects models

Multiple studies suggested that existing thermal comfort models inadequately predict occupants' individual thermal preferences. Personalised comfort models offer an alternative to conventional comfort models aiming to forecast individual's thermal preference. Implementation of these personalised models in occupant-centric control of heating, ventilation, and air-conditioning (HVAC) systems can enhance their performance.A promising technique for personalised comfort modelling is mixed-effects (ME) modelling. A ME model accounts for fixed effects, representing the trends in the general sample, and for random effects, representing variations of specific clusters in the data. In contrast to fixed-effects (FE) models, ME models can capture individual differences. However, its effectiveness in predicting occupants' thermal preferences based on field measurement data, as well as the influence of variations in ME models on prediction accuracy, remains to be thoroughly investigated.This study aims to assess the prediction accuracy of ME models in contrast to FE models using field measurement data, including thermal preference votes from 30 unique occupants. The prediction performance was evaluated across three testing scenarios, each representing a different application of the models. Furthermore, two random effect structures were tested for the ME model: an intercept-only model and an intercept and slope model. The results show that ME models, in comparison to FE models, achieve an improved prediction performance of 8.0 % on average and up to 28.4 % for individual occupants. Moreover, the addition of a random slope to the ME resulted in deteriorated predictions. Finally, occupants’ individual variations were determined with an uncertainty of 6 % after 20 observations.

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

Enhanced Control for Geothermal Air Conditioning System using LBHE with Half-Length Heat Exchanger and CO<sub>2</sub> Emission Reduction

Enhanced Control for Geothermal Air Conditioning System using LBHE with Half-Length Heat Exchanger and CO<sub>2</sub> Emission Reduction

Intelligent control of electric vehicle air conditioning system based on deep reinforcement learning

The existing AC system of EVs consumes a lot of energy in summer, resulting in reduced range. Additionally, the cabin temperature (Tcabin) fluctuates due to varying driving conditions. In this paper, an innovative cooling strategy is designed to address the above drawbacks using deep reinforcement learning algorithms. The primary objective of this strategy is to achieve high-precision temperature control while concurrently minimizing the energy consumption of the AC system. The strategy combines compressor speed (Ncompressor) and blower airflow (Bairflow) control to optimize the AC system's performance. The RL controller minimizes energy consumption by reducing Ncompressor while increasing Bairflow, thus ensuring a constant cooling capacity (Q). And it effectively reduces temperature fluctuations within the cabin. Finally, the RL control strategy was compared with on/off control and proportional integral differential (PID) control. The results showed that RL control performed well in the multi-objective optimization of the AC system. In terms of temperature control, it effectively reduces the amount of overshooting, and the minimum temperature fluctuation achieved through RL control is merely 0.07 °C, exhibiting an 84.4 % and 86.5 % decrease compared to traditional control methods. The average absolute temperature error stands at 0.06–0.07℃, maintaining a remarkable precision in preserving the target temperature. Furthermore, RL control not only ensures superior energy efficiency but also reduces energy consumption by up to 5.98 % and 7.65 % compared with traditional control methods.

Research on control strategy of rapid preheating for power battery in electric vehicle at low temperatures

Due to cold weather conditions, it is not advisable to use the remaining capacity of the power battery to heat it. Instead, the battery can only be preheated during charging when the battery power is almost consumed up. The traditional positive temperature coefficient (PTC) heating system combines the cockpit air conditioning and heating system with the low-temperature preheating system for the power battery cells. The PTC controller is integrated into the air conditioning control system. However, this integration requires the air conditioning to be activated in order to preheat the power battery. As a result, there is energy waste during the charging process. To address the aforementioned issues, this article proposes a rapid preheating system that utilizes an independent liquid circuit equipped with a high-voltage heater. The system achieves a preheating time of 2000 s for a ternary lithium battery with a capacity of 73kWh and a rated voltage of 770 V. Another important finding is that fluctuations in the current of the battery management system (BMS) occur during the rapid preheating process, leading to uncontrolled battery charging and discharging. The battery rapid preheating control strategy has been redesigned to rapidly heat the battery system by disconnecting the rapid charging relay of the high-voltage circuit, thereby prevents over-discharge and overcharge of the power battery. Experiments have shown that the BMS current increases or decreases in a stepwise manner, as expected by the design. The average temperature of the battery pack during onboard charge module slow charging and direct current fast charging is below 35 °C, and the maximum temperature difference is less than 6 °C. The proposed rapid preheating system and improved battery charging architecture can shorten the charging time and reduce energy consumption. This advancement will open up new possibilities for power battery protection and contribute to the development of lithium-ion batteries for electric vehicles at low temperatures.

A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism

Accurate and reliable cooling load forecasting is a prerequisite for air-conditioning system control and the basis for building-side energy management. Therefore, a hybrid prediction model of an improved bidirectional long short-term memory (BiLSTM) network based on principal component analysis network (PCANet) and attention mechanism (CNN-IBiLSTM-Attention) is proposed to predict the cooling load of large commercial buildings. First of all, the PCANet algorithm is used to analyze the sensitivity of the influencing factors. Then, the hybrid strategy improved whale optimization algorithm (HSIWOA) is used to optimize the hyperparameter of BiLSTM. At last, the performance of the proposed algorithm is verified by using the actual data of two commercial buildings in Xi'an. The results show that using the PCANet algorithm for sensitivity analysis avoids feature redundancy. HSIWOA is suitable for hyperparameter optimization of BiLSTM. Compared with the other three prediction models, CNN-IBiLSTM-Attention reduced the mean absolute percentage error (MAPE) of Building 1 and 2 test sets by 31.55 %, 55.59 %, and 60.58 % and 56.49 %, 60.3 %, and 67.37 %, respectively. The proposed prediction model has superior hyperparameter optimization ability, better model complexity, and stronger generalization ability. Therefore, the proposed prediction model becomes a reliable tool for predicting the cooling load of large commercial buildings.

Indoor thermal nonuniformity of atrium-centered public building: Monitoring and diagnosis for energy saving

Heating ventilation and air conditioning system accounts for over one third of building energy usage, especially for public buildings due to large indoor heat source and high ventilation and thermal comfort requirement compared to residential buildings. Natural ventilation shows high application potentials in public buildings because of its high-efficient ventilation effect and energy saving potential for indoor heat dissipation. In this paper, the building design is conducted for a science museum and library with atrium-centered natural ventilation consideration. The floor layout, building orientation and internal structure are optimized to make full use of natural ventilation for space cooling under local climatic conditions. A natural ventilation model is established through building field tests to evaluate the air flow and thermal environment under indoor and outdoor pressure differences. The preliminary results show that Building A's courtyard exhibited overheating issues, likely attributed to outdoor solar radiation, whereas Building B's courtyard experienced localized cooling, possibly due to indoor air conditioning system controls. Addressing these concerns necessitates modifications in courtyard design and structure. Moreover, both courtyards displayed vertical temperature gradients, emphasizing the need for effective management of outdoor heat influx and enhancements in indoor ventilation and shading strategies. To mitigate these issues, the study proposed three distinct roof design models and more refined indoor air conditioning system control strategies. The optimization of architectural design can achieve a maximum energy-saving rate of 46.54 %. Furthermore, aligning functional zones with thermal comfort areas was recommended to enhance overall building thermal comfort. The findings and proposed solutions from this study are anticipated to contribute to the enhancement of thermal comfort, energy efficiency, and vertical temperature distribution in large public buildings, catering to user requirements while reducing energy consumption. This research holds significant implications for advancing sustainability and environmental preservation in the realm of architecture.

Optimization of Air Supply Conditions for Large Space Radiant Air Conditioning Based on Response Surface Analysis

In large spaces, the comfort control of radiant air conditioning systems still has deficiencies. Mainly reliant on traditional parameter control methods, the interaction effects of diverse operating conditions are not taken into account, and the control results are still in need of optimization. To improve indoor air conditioning comfort control and address gaps in existing research, the response surface analysis method combined with numerical simulation is applied to determine the optimal air supply conditions for large-space radiant air conditioning. With the real model as a reference, simulations are carried out in Airpak, selecting four factors: supply air temperature, supply air humidity, air supply volume, and air supply angle for the response surface design. Discussions on interaction effects are carried out based on the simulated results of various scheme designs, determining eight groups of air supply schemes with the highest comfort. The research concludes that the response surface analysis method, considering the interactive effects of impacting factors, can lead to the strategy of high comfort and reasonable air supply conditions. Additionally, a set of recommendations are proposed for future practical application and promotion.

Successful application of predictive information in deep reinforcement learning control: A case study based on an office building HVAC system

Reinforcement Learning (RL), a promising algorithm for the operational control of Heating, Ventilation, and Air Conditioning (HVAC) systems, has garnered considerable attention and applications. However, traditional RL algorithms typically do not incorporate predictive information for future scenarios, and only a limited number of studies have examined the enhancement and impact of predictive information on RL algorithms. To address the issue of coupling RL and predictive information in HVAC system operation optimization, we employed an open-source framework to examine the impact of various predictive information strategies on RL outcomes. We propose a joint gated recurrent unit (GRU)-RL algorithm to handle situations where a time-series exists in state space. The results from four classic test cases demonstrate that the proposed GRU-RL method can reduce operating costs by approximately 14.5% and increase comfort performance by 88.4% in indoor comfort control and cost-management tasks. Moreover, the GRU-RL method outperformed the conventional DRL method and was merely augmented with prediction information. In indoor temperature regulation, the GRU-RL algorithm improves control efficacy by 14.2% compared to models without predictive information and offers an approximately 5% improvement over traditional network models. Finally, all models were made open source for easy replication and further research.

Safe-duration based ventilation and air conditioning system control strategy

The COVID-19 outbreak has shown the significance of ventilation and air conditioning systems in mitigating the transmission of the virus. Increasing air supply volume and utilizing indoor air purifiers are both effective measures to reduce the risk of infection. However, the specific air volume requirements in primary return air systems considering the impact of air purifiers installed within these systems have not been extensively studied. In this paper, the pathogenic aerosol emission rate was utilized to evaluate inhalation probability, taking into account the air purifier's removal efficiency. The notion of safe duration was additionally introduced as a criterion for quantifying indoor health and safety. A novel ventilation calculation method for different airborne infections to adjust safe duration was also presented. The findings suggested that the safe duration was mostly influenced by human-related factors, specifically occupant density and the effectiveness of facial masks in providing protection. When occupant density exceeded 0.35 people/m2, wearing facial masks with a higher protection coefficient was more effective in improving indoor safety and health than reducing occupant density. Moreover, enhancing the filtration efficacy of the air conditioning system's purifier can provide a level of protection comparable to wearing a regular mask.

A Comparison of Classical and Reinforcement Learning-based Tuning Techniques for PI controllers

This study compares two tuning techniques for Proportional-Integral (PI) controllers. The first strategy uses the Pole-Zero Cancellation method, a well-established technique in the field of dynamic systems control. The second strategy introduces an innovative approach by using a reinforcement learning-based technique to adaptively tune a PI controller. To compare these tuning methodologies, a Heating, Ventilation and Air Conditioning (HVAC) control system was selected as a case study to guarantee users’ thermal comfort. In both cases, the HVAC process was modelled as a first-order system without time delay. In addition, the performance of the proposed controllers analysed by evaluating temperature set-point tracking and disturbance rejection caused by the occupancy of people in the room. The results demonstrate that classical methods are efficient and quick to implement, while the use of RL also enables optimization of energy consumption and reduction of operating costs.

Smoothing cooling demand of buildings with PCM thermal batteries

Escalating energy tariffs and peak cooling demands due to climate change along with expanding use of variable renewable energy supply are presenting new challenges and opportunities for air conditioning system operation and control. This research presents the outcome of an investigation into the use of a thermal battery using salt-hydrate phase change material (PCM) in commercial buildings. A 1.2 m3 modular thermal battery using 15 °C melting temperature salt-hydrate PCM has been designed and fabricated. Its cooling performance and feasibility of integration into a chilled water-cooling system of commercial buildings has been comprehensively investigated. This storage unit can accommodate approximately 52 kWh of energy, featuring a rapid heat discharge rate of 32.58 kW during the initial 30 min to effectively address sudden cooling demands. The overall heat discharge rate closely aligns with simulation results, reaching approximately 96% accuracy. This has been achieved through optimisation of the heat exchanger design through mathematical simulation, detailed testing to match various operational scenarios and evaluation of economic and peak load shifting benefits. The results demonstrate the environmental and economic effectiveness of the PCM thermal battery as an independent component in building cooling systems. It provides a timely response to peak cooling demand and improves thermal comfort of the buildings.

A novel deep-learning framework for short-term prediction of cooling load in public buildings

Optimal control of heating, ventilation, and air conditioning (HVAC) systems, along with demand-side management, are both cost-effective methods in the process of energy conservation and carbon reduction. The successful implementation of these initiatives largely hinges on accurate cooling load predictions. Due to the complex nonlinear and dynamic time-varying nature of demand loads, however, it is a formidable challenge to accurately predict the cooling load. To address these issues, a novel deep learning-based prediction framework, aTCN-LSTM, is proposed. First, a gate-controlled multi-head temporal convolutional network is designed to capture the inherent nonlinear and local temporal features from the time series of cooling loads. Second, a sparse probabilistic self-attention mechanism is integrated with a bidirectional long short-term memory (BiLSTM) network to extract the long-term dependencies within the cooling load sequences. Finally, through integration with the proposed two components, the framework is developed and validated through a 14-month real cooling load forecasting problem for a 51-story hotel building in Guangzhou, China. Experiments and comparison studies demonstrate the effectiveness and superiority of the proposed method. The mean absolute percentage error of the proposed method's 1-step, 6-step, and 12-step prediction results is reduced by 27.48%, 14.05%, and 13.38%, respectively, compared with the state-of-the-art baseline model. Consequently, it stands poised to serve as an effective guide for HVAC chiller scheduling and demand management initiatives.

Design Automatic Air Conditioning System Control with Passive Infrared Receiver/motion sensor

Dr. Ferdinand Lumbantobing Airport faces serious challenges related to the waste of electrical energy in its operations. The quarterly report shows an inconsistent increase in electricity usage, which is largely due to human factors and some technical aspects. The biggest problem is the use of air conditioning and lights that do not need to be constantly active. This article takes a close look at the airport's efforts to address this waste of energy, focusing on the use of air conditioning and constantly burning lights in unused rooms. Measures such as monitoring, automation, sensors, employee extension, and smart appliance development are proposed to address these issues and achieve significant energy savings. This solution will help reduce airport operational costs, increase efficiency, and support environmental conservation.

Estimating perceived indoor air quality and environmental satisfaction using a camera

Although indoor environmental monitoring data are increasingly available, they often fail to accurately capture personal environmental perception, comfort, and satisfaction. This disconnection has led to a growing interest in using real-time feedback from building users to improve the control of heating, ventilation, and air-conditioning systems and reduce energy waste. However, traditional questionnaire-based surveys only provide a snapshot at the time of investigation and suffer from a variety of bias sources. To address this, this paper proposes a novel camera-based method for estimating perceived indoor air quality and environmental satisfaction by analysing facial expressions. We conducted indoor air pollution exposure experiments on 24 healthy adults in twin environmental chambers. Their changes in facial expressions were compared between different exposure conditions and the potential correlation between facial expressions and perceived air quality, environmental satisfaction, and self-reported sick building syndrome symptoms was explored. Preliminary results indicate that a common RGB camera can repeatably detect facial expressions from the same subject. Also, results from a subset of five subjects suggest moderate differences in concentration, calmness, contemplation, and tiredness expressions between the two air quality conditions.