Application of Data-Driven Methods for Heating Ventilation and Air Conditioning Systems

1. Introduction

At present, with the continuous global energy crisis, buildings, as a significant factor in energy consumption, have significant importance in achieving the energy-saving operation of buildings [1]. In buildings, the main energy-consuming pieces of equipment are heating ventilation and air conditioning (HVAC) systems. However, some of the limitations that HVAC systems currently face, such as control optimization [2], pattern recognition [3], and fault diagnosis [4], are difficult to solve effectively using traditional approaches. There is an urgent need for new technologies and methods to provide valid solutions. Data-driven methods utilize machine learning and artificial intelligence technology, from a data perspective, to construct a new solution with the ability to overcome the limitations of physical models [5,6]. Data-driven methods have been widely applied in fields such as image recognition [7] and speech recognition [8], and have already become a research hotspot in the field of HVAC systems [9,10]. The application of data-driven methods in the field of HVAC systems is gradually diversifying, and the depth of research is also gradually increasing.

This Special Issue, “Application of Data-Driven Methods for HVAC Systems”, is a collection of 13 interesting articles mainly covering three aspects: building environment data analysis, HVAC system control, and the application of data-driven methods. We are deeply honored to serve as guest editors and extend our heartfelt gratitude to all the authors who have contributed to this Special Issue. The papers authored by our colleagues will make substantial contributions to the application of data-driven methods within the field of HVAC systems.

2. Data Analysis in Experimental Research in Built Environments

Some studies have obtained data through conducting experiments related to HVAC systems. Thereby, some valuable conclusions have been drawn through various data analysis methods. Real-time measurements and simulations of the indoor thermal environment have been conducted. Indoor environmental data have thereby been obtained. The CFD method has been used to analyze internal pollutant diffusion patterns and concentration changes. Finally, regression analysis was used to study the relationship between the indoor thermal environment and aerosol diffusion paths. Moreover, an experiment has been conducted on an air-conditioned bed system. Experimental data on two heating forms, heat conduction-dominated and heat convection-dominated heating, were collected. After analyzing the experimental data, the results indicate that a higher temperature range is required when using convective heating. At the same time, convective heating temperature increases rapidly. However, it is easy to develop a dry atmosphere in the long term. Finally, it was concluded that heat conduction heating can bring better thermal comfort and higher energy efficiency.

A series of experiments have been conducted on the indoor environmental quality in kitchens. The indoor temperature, relative humidity, and CO and CO₂ concentrations in kitchens were measured under conditions of different cooking temperature. After an analysis of the experimental data, the results indicate that the heat and gas consumed during the cooking process are closely related to the temperature and CO and CO₂ concentration. In addition, natural wind has a significant impact on various indoor parameters. Experiments on the internal environment of granaries have been conducted. The latent heat and phase change temperature of the prefabricated phase change material (PCM) warehouse wall were optimized through numerical simulation. By analyzing experimental data, the thermal regulation performance of prefabricated panels on the grain warehouse wall was optimized. The results indicate that the application of PCM in granaries has advantages. Aguiar et al. [11] conducted real-time measurement and collection of temperature and relative humidity parameters during the transportation of corrosion-prone products. Optimizing parameters will help decision making in logistics and route management, as well as in the diagnosis and timely prevention of food losses. A total of 18 parameters regarding temperature and humidity were collected during the experiment, and the results indicate that cargo monitoring is of great significance for quality control and energy efficiency optimization in the supply chain.

3. Research on the Control of HVAC Systems

One of the major applications for data analysis of HVAC systems is system control and optimization. Basic information and electricity load data from a typical tourist village were collected. The power load characteristics of heating, cooling, and transition seasons have been studied. A cluster control conversion system using phase change energy storage was proposed through data analysis. The system’s control logic has been determined and established. Finally, the collected power load data were introduced into the model for practical case analysis, and its feasibility and effectiveness were verified. DEST software was used to study the effects of building envelope thermal parameters, the window-to-wall ratio, and the shape factor on the total energy consumption of buildings in different climate zones. A sensitivity analysis was conducted on different parameters to determine a passive design scheme suitable for substations in different climate zones. The analysis results also indicate that among the thermal parameters associated with the building envelope, the thickness of the roof insulation has the greatest impact on the energy consumption of substation buildings. The conclusions drawn from this study can offer architects valuable strategies and suggestions for energy saving in substations in different climate zones, and provide a reference for building energy-saving designs and selecting appropriate air-conditioning and heating equipment.

A mixed desiccant M-circulating cooling system with an HDH unit has been proposed for the simultaneous cooling and production of water. An analysis was conducted on the refrigeration and water production performance of the system for three typical operating modes. The indoor air temperature, relative humidity, and CO₂ concentration data were measured over a 4-week period. At the same time, the number of indoor residents, room occupancy time, and window opening were recorded. The results indicate that opening windows can effectively reduce the indoor carbon dioxide concentration, thus improving indoor air quality. However, at the same time, it is also necessary to pay attention to the impact of outdoor pollutants on the indoor environment.

4. Applications Based on Data-Driven Methods

Data driven methods are widely used in fault diagnosis, pattern recognition, scheme optimization, and data analysis research for HVAC systems [12,13]. Wang et al. [14] proposed a fault diagnosis model based on Bayesian networks. The proposed approach achieves the fusion of feature residuals and feature data in a single fault diagnosis model. The proposed hybrid method improves the fault diagnosis performance of chillers, with significant improvements in the diagnosis of refrigerant leakage faults and lubricating oil faults. An unsupervised clustering method for studying the running mode recognition of refrigeration, heating, and combined hot water–air conditioning systems is proposed. K-means, Gaussian mixed model clustering, and spectral clustering are three data-driven methods used to establish pattern recognition models for air-conditioning systems. A correlation analysis was used for the reduction of dimensionality in characteristic variables. The results indicate that the clustering model can identify defrosting patterns of air conditioning. The accuracy rate of pattern recognition reached 98.99%.

The indoor thermal environment and air quality of underground commercial buildings have been studied from a functional perspective. An optimal control strategy for airflow organization was proposed. This study provides a theoretical basis for the creation of a thermal environment and the organization and control of airflow in underground commercial buildings. The relationship between office park costs, energy consumption, and carbon emissions based on genetic algorithms was analyzed. A mathematical optimization model for the carbon reduction transformation of existing office parks has been established with the goal of reducing carbon throughout the entire life cycle of office parks. The results indicate that the established model can provide a comprehensive and optimized energy allocation plan to minimize carbon emissions at various investment costs.

5. Conclusions

The articles in this Special Issue include a series of studies from the perspective of data application. A new research approach has been introduced to HVAC systems, and some new strategies and models have been proposed, yielding valuable insights for improving performance and achieving energy-saving operations in HVAC systems. Furthermore, these results will also play a significant role in promoting the application of data-driven methods in field of HVAC systems.