Fuzzy Logic Control for Natural Ventilation: Design, Simulation, and Performance Analysis

Natural ventilation systems in commercial buildings often encounter control challenges due to their inability to manage and regulate airflow effectively. This difficulty arises from fluctuations in external and internal environmental factors, including variations in pressure, temperature, and wind velocity. As a result, occupants may experience decreased comfort levels and building energy may be wasted. Traditional control dynamics, characterised by fixed setpoints and limited input-output environments, lack the necessary flexibility to optimise natural ventilation effectively.
Fuzzy logic systems offer a promising solution to these issues by adeptly handling uncertainties and the dynamic interplay between factors such as temperature, humidity, and ventilation operations. The research detailed in this thesis explores the application of fuzzy logic to develop a control framework that addresses the challenges faced by natural ventilation systems, aiming for a more adaptive and efficient management approach.
The methodology adopted employs a mixed-method strategy to examine the integration of fuzzy logic protocols in optimising natural ventilation systems. It begins with a thorough review of existing literature, assessing energy consumption patterns, natural ventilation strategies, and advancements in fuzzy logic control systems. This review identifies key gaps and opportunities for innovation that inform the research design. Additionally, primary data was collected through on-site observations of two case study buildings, supported by interviews and analysis of ventilation performance metrics, which provided practical insights into the limitations of current ventilation systems and the potential benefits of fuzzy logic solutions.
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A fuzzy logic control system protocol was then developed using MATLAB/Simulink to dynamically manage five input variables: indoor and outdoor temperatures, relative humidity, CO2 levels, and wind speed. The rules incorporated addressed various scenarios: extreme, moderate, and low edge cases, to optimise the system's response. Simulations confirmed the efficacy of the model, which exhibited notable improvements in air quality and occupants’ comfort. Complementary simulations using Computational Fluid Dynamics (CFD) and the Integrated Environmental Solutions Virtual Environment (IESVE) assessed the thermal comfort performance of the test zone. A comparative analysis evaluated the extent of energy savings and the Likelihood of satisfaction using the thermal comfort index.
The findings indicated that fuzzy logic (rule-based system) could enhance natural ventilation and adaptive comfort by automating operations. For the current study, thermal comfort improved by 24.22% and energy consumption was reduced by approximately 31.26% compared to the conventional control system during the summer period, and by 53.41% compared to uncontrolled systems during the same period. This research program commenced during the COVID-19 pandemic period.