How can deep learning help to forecast the energy demand of machine tools?
A deep learning approach to electric load forecasting of machine tools
© PTW
PTW | 01.12.2021
Ihr Kontakt am PTW:
Bastian Dietrich M. Sc.
Forecasting the electric load of machine tools enables us to flexibly adapt our production to the fluctuating energy supply by renewable energies, an important aspect in the ongoing transition towards 100% renewables. During the HSM conference in Darmstadt, we had the opportunity to discuss our approach of deep learning techniques for energy forecasting of machine tools with the research community. The corresponding research paper “A deep learning approach to electric load forecasting of machine tools” was recently published in the MM Science Journal [1].
How can deep learning help to forecast the energy demand of machine tools?
A deep learning approach to electric load forecasting of machine tools
© PTW
PTW | 01.12.2021
Ihr Kontakt am PTW:
Bastian Dietrich M. Sc.
Forecasting the electric load of machine tools enables us to flexibly adapt our production to the fluctuating energy supply by renewable energies, an important aspect in the ongoing transition towards 100% renewables. During the HSM conference in Darmstadt, we had the opportunity to discuss our approach of deep learning techniques for energy forecasting of machine tools with the research community. The corresponding research paper “A deep learning approach to electric load forecasting of machine tools” was recently published in the MM Science Journal [1].
