Rising energy costs due to environmental degradation, climate change, global conflicts, and pandemics have prompted the need for efficient energy management. Edge devices are increasingly recognized for improving energy efficiency; however, their role as primary computing units remains underexplored. This study presents PreEdgeDB, a lightweight platform deployed on low-power edge devices to optimize energy usage in industrial complexes, which consume approximately 57.29% of South Korea’s total energy. The platform integrates real-time data preprocessing, time-series storage, and prediction capabilities, enabling independent operation at individual factories. A low-resource preprocessing module was developed to handle missing and anomalous data. For storage, RocksDB—a lightweight, high-performance key–value database—was optimized for edge environments. For prediction, Light Gradient Boosting Machine (LightGBM) was adopted due to its efficiency and high accuracy on limited-resource systems. The resulting model achieved a coefficient of variation of the root mean squared error (CV(RMSE)) of 14.36% and a prediction score of 0.8240. The total processing time from data collection to prediction was under 300 milliseconds. With memory usage below 150 MB and CPU utilization around 60%, PreEdgeDB enables fully autonomous energy prediction and analysis on edge devices, without relying on centralized servers.

A factory energy management system, based on information and communication technology, facilitates efficient energy management using the real-time monitoring, analyzing, and controlling of the energy consumption of a factory. However, traditional food processing plants use basic control systems that cannot analyze energy consumption for each phase of processing. This makes it difficult to identify usage patterns for individual operations. This study identifies steam energy consumption patterns across four stages of food processing. Additionally, it proposes a customized predictive model employing four machine learning algorithms—linear regression, decision tree, random forest, and k-nearest neighbor—as well as two deep learning algorithms: long short-term memory and multi-layer perceptron. The enhanced multi-layer perceptron model achieved a high performance, with a coefficient of determination (R²) of 0.9418, a coefficient of variation of root mean square error (CVRMSE) of 9.49%, and a relative accuracy of 93.28%. The results of this study demonstrate that straightforward data and models can accurately predict steam energy consumption for individual processes. These findings suggest that a customized predictive model, tailored to the energy consumption characteristics of each process, can offer precise energy operation guidance for food manufacturers, thereby improving energy efficiency and reducing consumption.

The network security of Plug-and-Charge (PnC) technology in electric vehicle charging systems is typically achieved through the well-known Transport Layer Security (TLS) protocol, which causes high communication overhead. To reduce this overhead, a differential authentication method employing different schemes for individual users has been proposed. However, decisions use a simple threshold approach and no quantitative performance evaluation should be made. In this study, we determined each user's trust using several machine learning algorithms with their charging patterns and compared them. The experimental results reveal that the proposed approach outperforms the conventional approach by 41.36% in terms of round-trip time efficiency, demonstrating its effectiveness in reducing the TLS overhead. In addition, we show the simulation results for three user authentication methods and capture the performance variations under CPU busy waiting scenarios.

The factory energy management system based on information and communication technology facilitates efficient energy management using real-time monitoring, analyzing, and controlling the energy consumption of a factory. However, traditional food processing plants use basic control systems that cannot analyze energy consumption for each phase of processing. This makes it difficult to identify usage patterns for individual operations. This study identifies steam energy consumption patterns across four stages of food processing and proposes a customized predictive model employing four machine learning algorithms (linear regression, decision tree, random forest, and k-nearest neighbor) as well as two deep learning algorithms (long short-term memory and multi-layer perceptron). The enhanced multi-layer perceptron (MLP) model achieved high performance, with a coefficient of determination (R2) of 0.9418, a coefficient of variation of root–mean–square error (CVRMSE) of 9.49%, and a relative accuracy of 93.28%. The results of this study demonstrate that straightforward data and models can accurately predict steam energy consumption for individual processes. These findings suggest that a customized predictive model, tailored to the energy consumption characteristics of each process, can offer precise energy operation guidance for food manufacturers, thereby improving energy efficiency and reducing consumption.