Recurrent Neural Network-Based Dynamic Cooling Adjustment for Energy-Efficient Air Conditioning with IoT Sensors

This research introduces a novel approach to the problem of air conditioning (AC) system energy efficiency. It presents a dynamic method for cooling control by combining recurrent neural networks (RNNs) with Internet of Things (IoT) sensors. The suggested approach uses real-time environmental data collected by IoT sensors, in contrast to traditional systems that stick to static cooling schedules. To predict the best cooling parameters, RNNs examine this data in conjunction with past trends. The AC system may adjust the amount of cooling it provides in response to the current weather. This adaptive technique reduces energy use while simultaneously increasing comfort. By comparing the suggested technique to more conventional ones, experimental validation shows that it is effective and results in significant energy savings. This technology has the potential to improve the efficiency and longevity of AC systems in a wide variety of settings, according to the results. This study makes a significant contribution to the continuous quest for energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) system solutions by integrating advanced neural network modeling with novel IoT technologies.