Optimization of Energy Demand Management in Cold Storage Systems: Advances through the Integration of IoT, Machine Learning, and AI

Cold storage facilities play a crucial role in preserving perishable products across various industries, including food, pharmaceuticals, and agriculture. The high energy consumption of refrigeration systems in these facilities necessitates efficient energy management. This article uses Long Short-Term Memory (LSTM) models in a novel way to predict temperature and power demand variations in cold storage systems, leading to more accurate forecasts. The research improves demand response applications and achieves significant energy savings through real-world deployment by combining real-time IoT sensor data and deep learning. The study provides scalable framework that can be applied to a variety of industrial settings, helping to promote more sustainable energy management practices by optimizing power usage and lowering operational costs. Various methodologies and models were evaluated to predict and manage temperature variations and energy demand efficiently. The results demonstrate significant enhancements in energy efficiency, reduced energy consumption, and optimized resource usage. Among the evaluated machine learning techniques, deep neural networks, specifically Long Short-Term Memory (LSTM) models, showed superior accuracy and responsiveness. Mini-ROCKET and DrCIF, along with their precision and lower mean absolute error (MAE), make reliable predictions possible with a MAPE below 0.2 having overall accuracy of this model is 96%. Additionally, the adaptability of these models to abrupt operational changes was analyzed, highlighting their advantages and limitations. The paper concludes with recommendations for future research and development, promoting the adoption of advanced and sustainable technologies in energy management for cold storage systems.