Prediction of granular time-series energy consumption for manufacturing jobs from analysis and learning of historical data

Abstract

In the manufacturing sector, the consideration of energy consumption during the scheduling and execution of jobs can offer significant benefits from an infrastructural and financial perspective. While numerous methods have been proposed for predicting the energy consumption of manufacturing machinery, they typically do not treat them as dynamic pieces of equipment which can lead to issues with long term accuracy. Furthermore, these models produce predictions at a high level of abstraction which can lead to sub-optimal utilization. This paper addresses these shortcomings and presents a new methodology based around the usage and inference of historical energy data. Multiple energy profiles for identical jobs are stored along with information regarding the machines mechanical conditions, allowing the system to compensate for machine-related changes to the energy consumption. Where historical data is lacking, analysis of how the machine's condition affects job energy consumption over time, allows for the use of Support Vector Regression to generate temporary synthetic energy profiles compensated for probable machine conditions.