Machine learning based very short term load forecasting of machine tools. (15th October 2020)

• Record Type: Journal Article
• Authors: Dietrich, Bastian; Walther, Jessica; Weigold, Matthias; Abele, Eberhard
• Is Part Of: Applied energy. Volume 276(2020)
• Publication Date: 2020-10-15
• Subjects: Energy flexibility -- Load forecasting -- Machine tool -- Machine learning -- Feature engineering; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115440
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Accurate transferrable machine learning based load forecasting of machine tools. Automated data preprocessing, feature construction and selection process. Time lag and moving average feature construction increases forecasting accuracy. Autocorrelation function is a valuable information source of feature construction. Abstract: With the ongoing integration of renewable energies into the electrical power grid, industrial energy flexibility gains importance. To enable demand response applications, knowledge about the future energy demand is necessary. This paper presents a machine learning process to forecast the very short term load of two machine tools, which can be utilized as a decision support basis for control schemes and measures to increase energy flexibility and decrease energy cost in manufacturing. The presented process is developed and evaluated on production machines in a research factory. The results indicate that the developed machine learning process is feasible and creates an accurate very short term load forecasting model for different production machines. It can be used as a blueprint to develop load forecasting models for other production machines using the historic load profile and various machine and process data. A combination of time series features and an Artificial Neural Network proves to be the most robust model regarding the presented machine tools with achieved coefficients of determination between 0.57 and 0.64 for a 100 step forecast. Improvements are still needed regarding the forecasting accuracy, especially of load peaks, for which different measures are proposed.