Prediction of energy consumption for manufacturing small and medium-sized enterprises (SMEs) considering industry characteristics.

There has been a growing demand for energy consumption statistics in the manufacturing industry to establish national energy and greenhouse gas policies. Despite its importance, the Korean government faces significant challenges in collecting energy data at a facility level in a precise and timely manner. To address the lack of timely data, this paper employs machine-learning models to predict the annual total energy consumptions of each manufacturing facility. We first designed four prediction models that take into account the characteristics and energy consumption behaviors of industry sub-sectors. As input variables, these prediction models mainly included electricity consumption, employee size, energy types, gas consumption and other accessible data. Finally, we conducted numerical experiments on approximately 100,000 facilities and evaluated the prediction performance of various machine-learning algorithms such as linear regression, decision tree regression, random forest regression, gradient boost regression, and extreme gradient boosting regression. The numerical experiments provided insights into which model and algorithm offer the best prediction performance for each industry sub-sector. In addition, we identified the important variables for predicting total energy consumption, revealing that not only electricity but also various other energy sources and variables representing industry-specific characteristics play a crucial role in improving prediction performance. • This paper predicts the annual total energy consumption of individual manufacturing facilities in Korea. • Several prediction models capable of considering industry-specific features are presented. • This paper employs machine-learning models. • The numerical experiments provided insights into which model and algorithm offer the best prediction performance. • Not only electricity but also variables representing industry-specific characteristics play a crucial role. [ABSTRACT FROM AUTHOR]