The building and construction industry is responsible for 40% of global energy. While it consumes the most energy globally, it contributes to 30% of total carbon dioxide emissions. The drive to minimize building energy usage and associated greenhouse gas emissions has functioned as a catalyst in creating advanced computational methods for energy-efficient building design, management, and control. HVAC stands for heating, ventilation, and air- conditioning systems, and these systems create the hearts of buildings.
HVAC systems are the primary source of energy consumption in buildings and are ideal candidates for a significant reduction in energy demands. Over the last few decades, we have seen considerable progress in using machine learning and computational intelligence (CI) techniques for HVAC design, control, management, optimization, problem identification, and diagnostics.
To ensure high efficiency and increase the life of HVAC systems, a stable air handling unit (AHU) is vital. This article provides a detailed and critical examination of how machine learning revolutionizes HVAC diagnostics and repair and the applications of CI approaches for HVAC system prediction, optimization, control, and diagnostics.
HVAC Systems, Optimization, and Fault Detection and Diagnosis:
The HVAC systems are the most complex and energy-intensive systems in a building. Any fault in the system’s functioning, such as a defective cooling tower fan, compressor failure, or a stuck damper, might result in energy waste and a decrease in the system’s coefficient of performance (COP). Detecting these issues is difficult due to the complexity of the HVAC systems, which require extensive inspections.
Propose that by repairing HVAC faults, energy can be saved by 20% to 30%. As a result, fault detection and diagnostics (FDD) techniques can be used to monitor HVAC systems’ performance while detecting abnormalities or problems. Extensive FDD research on HVAC has been conducted in recent years. Three FDD approaches for designing systems are model-based, rule-based, and data-driven HVAC techniques.
Building occupancy management systems with advanced analytics software and machine learning capabilities is the most effective. These platforms constantly analyze sensor data to detect possible problems and recommend solutions. With a thorough understanding of building conditions, you can solve occupancy-related concerns and devise sensible solutions to improve future performance.
For example, as occupants enter a meeting room, ventilation can automatically increase, and they can be alerted when a restroom has been used several times. In other words, smart building occupancy management makes equipment and employees more responsive to people’s real-time needs. Analytics software with machine learning skills will understand the complexities of a building’s operations over time, allowing it to enhance conditions and efficiency even more.
HVAC systems consume the most energy in both residential and business buildings. As a result, energy efficiency efforts aimed at HVAC can result in significant savings. However, energy-saving HVAC changes should not impact interior environmental quality, or they may create pain and health problems. This has a detrimental influence on productivity in business settings as well.
Effective Fault Detection and Diagnosis of HVAC Systems using Machine Learning Approaches:
HVAC systems work in dynamic conditions and can therefore be unpredictable due to unexpected disturbances and/or breakdowns. This entails making buildings smarter, which can be accomplished by fully using sensor-based infrastructure and applying FDD approaches. FDD is an analytic tool that identifies faults in HVAC systems and provides advice about how to address those problems.
FDD in HVAC systems is crucial because of several variables. Faults can lead to poor indoor air quality, insufficient heating or cooling, or both. These issues might significantly impact the occupants’ health and well-being. Higher energy expenses for building owners and operators and higher energy bills result from inefficient energy usage.
Artificial intelligence and machine learning have promising applications in HVAC since they can increase energy efficiency and indoor environmental quality. AI can optimize airflow, temperature, and humidity, preserving air quality while consuming the least energy. Since building conditions are always changing, it is difficult to control an HVAC system properly.
Think about the following aspects:
- The external temperature always changes, influencing the heating or cooling necessary to maintain a comfortable inside temperature.
- People frequently enter and exit buildings, affecting ventilation and temperature control.
- Indoor activities have an impact on HVAC requirements. A commercial kitchen, for example, requires more ventilation and cooling than an office of the same size.
FDD fully uses data-driven methods such as machine learning, artificial neural networks (ANN), and support vector machines (SVM). As a classifier, Support Vector Machine (SVM) is very efficient and widely used. Inefficient HVAC systems can also cause equipment to wear out early and requires more frequent repairs, raising maintenance expenses.
Another critical aspect is safety. Several hazardous compounds, such as fuels and refrigerants, are used in today’s HVAC systems. If HVAC systems are not properly maintained and managed, leaks, fires, and other safety issues can occur. With the aid of machine learning HVAC diagnostic and repair, potential safety issues may be identified and corrected before they occur.
As a result, machine learning in FDD is critical for recognizing and fixing faults with HVAC systems to keep them operational and avoid these harmful consequences. Machine learning can revolutionize the HVAC industry.
