Datenbedarfe für eine energie-flexible Produktionsplanung und -steuerung

Schuh, Günther; Schmitt, Robert; Prote, Jan-Philipp; Ellerich, Max; Thomas, Katharina; Chhor, Jimmy; Sauermann, Frederick; Funk, Franziska K.

doi:10.3139/104.111966

Cited by 1 publication

(1 citation statement)

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“…The exhaustion of fossil energy sources, increased use of renewable energy, restrictions due to government regulations or international interstate crises and conflicts, lead to higher energy prices and a weakening of confidence and financial markets [4,10,16]. In addition, as renewable energy is expanding and a generation-based consumption is increasing due to volatility of renewable energies, industry is increasingly forced to flexibly adapt to energy supply [17,18].…”

Section: Introductionmentioning

confidence: 99%

Increasing Energy Efficiency and Flexibility by Forecasting Production Energy Demand Based on Machine Learning

Trenz

Hoffmann

Lange

et al. 2023

Lecture Notes in Mechanical Engineering

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The ability of manufacturing companies to compete depends strongly on the efficient use of production resources and the flexibility to adapt to changing production conditions. Essential requirements for the energetic infrastructure (EGI) result from the production itself, e.g., security of supply, efficiency and peak shaving. Since production always takes priority and must not be disturbed, the flexibility potential in terms of energy efficiency lies primarily in the EGI. Based on this, strategies will be developed that support companies in increasing their efficiency and flexibility by optimizing the configuration and operation of the EGI, while production processes are reliably supplied and not adapted. This is reached with intelligent operation strategies for the heating and cooling network based on forecasts, the use of energy storage systems, and the coupling of energy sectors. This paper presents an approach for energy forecasts used for the optimization of operation strategies. Hence, an energy-forecast-tool was developed, which is used for the prediction of electrical and thermal loads depending on the expected production. Therefore, machine learning models are trained with past weather, energy, and production data. Using production planning data and weather forecasts, the model can predict energy demands as input for an EGI optimization.

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