Computationally efficient integrated design and predictive control of flexible energy systems using multi‐fidelity simulation‐based Bayesian optimization

2021 ◽  
Author(s):  
Farshud Sorourifar ◽  
Naitik Choksi ◽  
Joel A. Paulson
Keyword(s):  
Predictive Control ◽  
Integrated Design ◽  
Energy Systems ◽  
Bayesian Optimization ◽  
Computationally Efficient ◽  
Simulation Based

scholarly journals Adaptive Simulation-Based Training of Artificial-Intelligence Decision Makers Using Bayesian Optimization

2018 ◽  
Vol 15 (2) ◽  
pp. 38-56
Author(s):  
Brett Israelsen ◽  
Nisar Ahmed ◽  
Kenneth Center ◽  
Roderick Green ◽  
Winston Bennett
Keyword(s):  
Artificial Intelligence ◽  
Decision Makers ◽  
Bayesian Optimization ◽  
Adaptive Simulation ◽  
Simulation Based

Simulation und Optimierung kombiniert/A simulation-based framework for operational optimization of industrial energy systems

2020 ◽  
Vol 110 (01-02) ◽  
pp. 12-17
Author(s):  
Niklas Panten ◽  
Heiko Ranzau ◽  
Thomas Kohne ◽  
Daniel Moog ◽  
Eberhard Abele ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Cost Savings ◽  
Energy Systems ◽  
Decisive Role ◽  
Optimization Methods ◽  
Energy System ◽  
Significant Cost ◽  
Operational Optimization ◽  
Simulation Based ◽  
Industrial Energy

Die optimierte Betriebsweise von industriellen Energiesystemen ist eine Schlüsseltechnologie, um signifikante Kosteneinsparpotenziale durch Steigerung der Energieeffizienz und -flexibilität zu heben. Weil dabei eine Vielzahl dynamischer und stochastischer Einflüsse berücksichtigt werden müssen, spielt die Simulation des Energiesystems eine entscheidende Rolle. Zur Evaluierung unterschiedlicher Betriebsoptimierungsverfahren wird ein simulationsgestütztes Framework vorgestellt, welches bei KI (Künstliche Intelligenz)-Algorithmen unter anderem für das Anlernen mit synthetischen Daten verwendet werden kann.   The optimized operation of industrial energy systems is a key technology to unlock significant cost savings by increasing energy efficiency and flexibility. Since a variety of dynamic and stochastic influences must be considered, the simulation of the energy system plays a decisive role. A simulation-based framework is presented for evaluating various operational optimization methods, which can also be used for learning based on synthetic data with AI (artificial intelligence) algorithms.

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