Energy analysis is an essential topic within a sustainable manufacturing strategy. To better understand the energy demand in a manufacturing plant, consideration of trends and patterns of energy consumption, and making predictions based on historical data is a promising approach. Time series analysis is a favorable method to be used; because of the rapid development in metering/sensor technology and computational systems, time series analysis can now be deployed on larger-scale systems. However, the application of time series models to manufacturing plant energy modeling is rare due to complexity. This paper augments traditional time series forecasting for manufacturing energy study, with the consideration of data trend and patterns, exogenous influential inputs, and potential overfitting issues. Automotive manufacturing plant electricity demand was used as a study case for the proposed modeling approach validation. In this research, time series analysis is shown to effectively capture the increasing trend and seasonal patterns in the energy demand of a vehicle manufacturing plant. Models with exogenous inputs show a better accuracy as measured by Mean Square Error, and are more robust to sudden deviations.
Feng, Lujia; Mears, Laine; and Schulte, Joerg, "Energy Demand Forecasting in an Automotive Manufacturing Plant" (2016). Publications. 2.