5 citations · 9 across the 15 of their papers we have counts for
15 papers
An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models
Ari Luna Rueda, Eike Cramer, Klaus Hellgardt +1
We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into c…
Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?
Clemens Kortmann, Eike Cramer
Adversarial attacks are crafted data manipulations that aim to deteriorate the outcomes of prediction or decision-making algorithms. In the energy systems literature, adversarial a…
Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics
Marah Almanasreh, Alexander Mitsos, Eike Cramer
Accurate prediction of polymerization dynamics is essential for process design, control, and optimization. Yet, purely mechanistic models require labor-intensive parameterization o…
Iterative Model-Learning Scheme via Gaussian Processes for Nonlinear Model Predictive Control of (Semi-)Batch Processes
Tai Xuan Tan, Alexander Mitsos, Eike Cramer
Batch processes are inherently transient and typically nonlinear, motivating nonlinear model predictive control (NMPC). However, adopting NMPC is hindered by the cost and unavailab…
Bayesian Optimization of Partially Known Systems using Hybrid Models
Eike Cramer, Luis Kutschat, Oliver Stollenwerk +2
Bayesian optimization (BO) has gained attention as an efficient algorithm for black-box optimization of expensive-to-evaluate systems, where the BO algorithm iteratively queries th…
Data-Driven Conditional Flexibility Index
Moritz Wedemeyer, Eike Cramer, Alexander Mitsos +1
With the increasing flexibilization of processes, determining robust scheduling decisions has become an important goal. Traditionally, the flexibility index has been used to identi…
Robust Energy System Design via Semi-infinite Programming
Moritz Wedemeyer, Eike Cramer, Alexander Mitsos +1
Time-series information needs to be incorporated into energy system optimization to account for the uncertainty of renewable energy sources. Typically, time-series aggregation meth…
Least Squares and Marginal Log-Likelihood Model Predictive Control using Normalizing Flows
Eike Cramer
Real-world (bio)chemical processes often exhibit stochastic dynamics with non-trivial correlations and state-dependent fluctuations. Model predictive control (MPC) often must consi…
Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows
Hannes Hilger, Dirk Witthaut, Manuel Dahmen +3
Trading on the day-ahead electricity markets requires accurate information about the realization of electricity prices and the uncertainty attached to the predictions. Deriving acc…
Physics-inspired machine learning for power grid frequency modelling
Johannes Kruse, Eike Cramer, Benjamin Schäfer +1
The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation…
Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows
Eike Cramer, Dirk Witthaut, Alexander Mitsos +1
Electricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of r…
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators
Eike Cramer, Leonard Paeleke, Alexander Mitsos +1
We present a specialized scenario generation method that utilizes forecast information to generate scenarios for day-ahead scheduling problems. In particular, we use normalizing fl…
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows
Eike Cramer, Felix Rauh, Alexander Mitsos +2
To model manifold data using normalizing flows, we employ isometric autoencoders to design embeddings with explicit inverses that do not distort the probability distribution. Using…
Validation Methods for Energy Time Series Scenarios from Deep Generative Models
Eike Cramer, Leonardo Rydin Gorjão, Alexander Mitsos +3
The design and operation of modern energy systems are heavily influenced by time-dependent and uncertain parameters, e.g., renewable electricity generation, load-demand, and electr…
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows
Eike Cramer, Alexander Mitsos, Raul Tempone +1
Neural networks-based learning of the distribution of non-dispatchable renewable electricity generation from sources such as photovoltaics (PV) and wind as well as load demands has…