Reducing energy consumption in air conditioning systems, a fuzzy logic-based optimization approach

The need for more intelligent air conditioning solutions is growing as global temperatures rise and energy efficiency concerns increase. In order to adapt dynamically to changing outdoor environmental factors like temperature fluctuations, humidity variations, and voltage instability, traditional air conditioning systems (ACS) usually rely on static control mechanisms. Rigid control paradigms like these result in ineffective operations, excessive energy use, and decreased user comfort. In order to get around these restrictions, this paper presents a practical fuzzy logic controller (FLC) that is specifically made to optimise compressor speed ratios and fan speed in real-time. Our method uses adaptive fuzzy logic, which offers greater flexibility in managing environmental uncertainties and disturbances than current controllers that either rely on fixed parameters or intricate machine learning models. Based on thorough simulations and performance evaluations, our suggested approach shows a significant 20–25 % decrease in energy consumption when compared to traditional systems. The main innovation is its straightforward but reliable fuzzy-based rule system, which strikes a balance between user comfort, adaptability, and energy efficiency and lays the groundwork for further intelligent climate-control advancements. • ACS controller for real-time parameter optimization based on fuzzy logic. • 20–25 % less energy was used than with traditional ACS. • Adaptability is increased through dynamic compressor and fan speed adjustment. • Temperature, humidity, and voltage fluctuations are managed by a robust fuzzy logic controller. • The suggested strategy strikes a balance between energy-efficient cooling and user comfort.