Forecasting Trends in Electrical Energy Efficiency in the Food Industry

Saksirin Chinnaket, Pasapitch Chujai Michel and Pakpoom Chansri ()
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Saksirin Chinnaket: Division of Electrical Technology Education, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
Pasapitch Chujai Michel: Division of Electrical Technology Education, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
Pakpoom Chansri: Division of Electrical Technology Education, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand

Energies, 2025, vol. 18, issue 21, 1-13

Abstract: Trends in electrical energy efficiency are key factors influencing production costs in food industry plants, as all production equipment relies on electricity. Accurate forecasting is essential for predicting future consumption and enabling effective energy management. This study aims to analyze and forecast trends in electrical energy efficiency in the food industry. Production and electricity consumption data from January 2022 to December 2023 were used to calculate the difference in electrical energy (DIFF) and the cumulative sum of electrical energy differences (CUSUM), which served as the basis for forecasting. The Long Short-Term Memory (LSTM) model, based on the deep learning approach, was employed to simulate the algorithmic patterns of electrical energy data in the food industry. Its forecasting performance was then compared with two alternative models, namely decomposition and logistic regression, using evaluation data from January to December 2024. Model accuracy was assessed using the Mean Absolute Percentage Error (MAPE) criterion. The results revealed that the decomposition model achieved lower MAPE values for both DIFF (14.47%) and CUSUM (24.13%), while the logistic regression model yielded higher MAPE values of 73.70% and 66.85%, respectively. Therefore, the decomposition model was identified as the most suitable method for forecasting electrical energy consumption trends in the food industry, providing higher accuracy and reliability than logistic regression. Forecasting energy consumption trends using the decomposition model can support strategic energy planning to enhance efficiency, reduce costs, and promote the sustainable development of the food industry in the future.

Keywords: decomposition; logistic regression; Mean Absolute Percent Error (MAPE); Cumulative Sum Chart (CUSUM); Long Short-Term Memory (LSTM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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