The industrial sector accounts for a significant proportion of total energy consumption. Factory Energy Management Systems (FEMSs) can be a measure to reduce energy consumption in the industrial sector. Therefore, machine learning (ML)-based electricity and liquefied natural gas (LNG) consumption prediction models were developed using data from a food factory. By applying these models to FEMSs, energy consumption can be reduced in the industrial sector. In this study, the multilayer perceptron (MLP) algorithm was used for the artificial neural network (ANN), while linear, radial basis function networks and polynomial kernels were used for support vector regression (SVR). Variables were selected through correlation analysis with electricity and LNG consumption data. The coefficient of variation of root mean square error (CvRMSE) and coefficient of determination (R²) were examined to verify the prediction performance of the implemented models and validated using the criteria of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers Guideline 14. The MLP model exhibited the highest prediction accuracy for electricity consumption (CvRMSE: 17.35% and R²: 0.84) and LNG consumption (CvRMSE: 12.52% and R²: 0.88). Our findings demonstrate it is possible to attain accurate predictions of electricity and LNG consumption in food factories using relatively simple data.

Amidst an energy crisis stemming from increased energy costs and the looming threat of war, there has been a burgeoning interest in energy conservation and management worldwide. Industrial complexes constitute a significant portion of total energy consumption. Hence, reducing energy consumption in these complexes is imperative for energy preservation. Typically, factories within similar industries aggregate in industrial complexes and share similar energy utilities. However, they often fail to capitalize on this shared infrastructure efficiently. To address this issue, a network system employing a virtual utility plant has been proposed. This system enables proactive measures to counteract energy surplus or deficit through AI-based predictions, thereby maximizing energy efficiency. Nevertheless, deploying conventional server systems within factories poses considerable challenges. Therefore, leveraging edge devices, characterized by low power consumption, high efficiency, and minimal space requirements, proves highly advantageous. Consequently, this study focuses on constructing and employing data collection and AI systems to utilize edge devices as standalone systems in each factory. To optimize the AI system for low-performance edge devices, we employed the integration-learning AI modeling technique. Evaluation results demonstrate that the proposed system exhibits high stability and reliability.

Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base model (with all hyperparameters set to the default values) using the coefficient of variation of root-mean-square error (CvRMSE) and coefficient of determination (R²) based on the Measurement and Verification Guideline evaluation metrics. To confirm the model’s industrial applicability, its predictions were compared with values measured at a small-to-medium-sized food factory. The optimized model performed better than the base model, achieving a CvRMSE of 12.30% and an R² of 0.94, and achieving a predictive accuracy of 91.49%. By predicting energy consumption, these findings are expected to promote the efficient operation and management of energy in the food industry.