AI-Driven Smart Air Conditioning System for a Sustainable and Energy-Efficient Industrial Future

AI-Driven Smart Air Conditioning System for a Sustainable and Energy-Efficient Industrial Future

연구 분야: Infrastructure

학회: International Conference on Innovative Intelligent Industrial Production and Logistics

This paper examines short-term predictions of industrial air-conditioning loads using the data from smart meters. Using IoT, data can be collected from sensors that measure temperature, humidity, and other relevant parameters. This enables real-time monitoring of environmental conditions. We present a comprehensive architecture for an energy-efficient and sustainable solar air conditioning system for an efficient industrial future. To optimize operational efficiency and reduce energy costs, this system must predict energy consumption. Various machine learning models were explored and tested, including Convolutional Neural Networks - Long Short-Term Memory (CNN-LSTM), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU) and TimeGPT to identify the most effective approach for energy prediction. RMSE and MAPE metrics showed that the TimeGPT model outperformed all other models in terms of accuracy and reliability in forecasting energy consumption. TimeGPT’s results provide evidence that industrial environments can improve their energy efficiency and sustainability by using the TimeGPT model. In addition to better energy management, this approach reduces costs and reduces carbon footprints. Energy consumption can is therefore predicted to optimize air conditioner operation, plan preventive maintenance, detect potential problems, and take corrective action to reduce the unnecessary consumption of energy.