Implementation of central monitoring and control system to improve air-conditioning system efficiency
Mechanical Engineering
The diploma thesis examines ways to improve the performance efficiency of a central air-conditioning system through the implementation of a central monitoring and control system (CNS). The introduction outlines the field of air-conditioning for large buildings and the challenges of energy efficiency, reliability, and operational flexibility.
The problem of inefficient operation of existing systems is defined, as these systems often fail to exploit the potential of modern automation and data analytics. The aim of the thesis is to develop solutions for optimizing the operation of central air conditioner by integrating a CNS, with the goals of reducing energy consumption, improving occupant comfort, and increasing reliability.
The core assumption is that a properly configured CNS enables measurable savings and improved operational performance.
The research approach combines quantitative methods—such as measuring energy consumption and system responsiveness—with qualitative methods, including case studies and an analysis of challenges in CNS implementation. The technical section provides a detailed description of the functioning of central air conditioning, thermodynamic processes, hydraulic manifold, and control of terminal units.
Key components are presented—such as chillers, circulation pumps, fan-coil units, sensors, and the CNS—and their role in efficient energy transfer and control. Particular attention is given to the shortcomings of existing solutions, including poor device integration, inadequate sensor coverage, hydraulic imbalance, and underutilization of available data.
The thesis also addresses energy efficiency and energy classes, highlighting the limitations of standardized indicators and the need for a holistic system-level assessment. CNS functionalities are presented, together with its advantages in automation, comfort personalization, data analytics, and benchmarking.
In the practical part, the impacts of the CNS on the reliability of air conditioners are analyzed using statistical and machine-learning models, as well as comparative analysis before and after CNS installation.
The concluding chapter discusses the economic and sustainability aspects of CNS adoption, including cost analysis, assessment of the environmental impact of optimized air-conditioning, and life-cycle assessment (LCA) of the equipment.
The regulatory framework affecting the design and implementation of such systems is also presented. The conclusion confirms the hypotheses, highlights the practical implications of the findings, and provides recommendations for further research.
