1 code implementation • 31 Aug 2023 • Xinyi Wen, Mehrnaz Anvari, Leonardo Rydin Gorjao, G. Cigdem Yalcin, Veit Hagenmeyer, Benjamin Schafer
Furthermore, we emphasize the need to analyze data from a large range of synchronous areas to obtain generally applicable models.
no code implementations • 25 Aug 2023 • Xinliang Dai, Junyi Zhai, Yuning Jiang, Yi Guo, Colin N. Jones, Veit Hagenmeyer
This paper introduces a distributed operational solution for coordinating integrated transmission-distribution (ITD) systems regarding data privacy.
1 code implementation • 19 Jun 2023 • Matthias Hertel, Maximilian Beichter, Benedikt Heidrich, Oliver Neumann, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer
We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients.
no code implementations • 30 Mar 2023 • Sebatian Pütz, Johannes Kruse, Dirk Witthaut, Veit Hagenmeyer, Benjamin Schäfer
Therefore, the electrical power grid's operation and energy and balancing markets are subject to strict regulations.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • 6 Feb 2023 • Benedikt Heidrich, Kaleb Phipps, Oliver Neumann, Marian Turowski, Ralf Mikut, Veit Hagenmeyer
Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly.
no code implementations • 3 Feb 2023 • Kaleb Phipps, Benedikt Heidrich, Marian Turowski, Moritz Wittig, Ralf Mikut, Veit Hagenmeyer
More specifically, we apply a cINN to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast to generate accurate probabilistic forecasts.
1 code implementation • 9 Jan 2023 • Moritz Frahm, Thomas Dengiz, Philipp Zwickel, Heiko Maaß, Jörg Matthes, Veit Hagenmeyer
In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability.
no code implementations • 13 Dec 2022 • Stefan Meisenbacher, Benedikt Heidrich, Tim Martin, Ralf Mikut, Veit Hagenmeyer
To tackle the problem of missing information about the PV mounting configuration, we use new data that become available during operation to adapt the ensemble weights to minimize the forecasting error.
1 code implementation • 6 Dec 2022 • Rebecca Bauer, Tillmann Mühlpfordt, Nicole Ludwig, Veit Hagenmeyer
The increase in renewable energy sources (RESs), like wind or solar power, results in growinguncertainty also in transmission grids.
no code implementations • 27 Sep 2022 • Thorbjørn Lund Onsaker, Heidi S. Nygård, Damià Gomila, Pere Colet, Ralf Mikut, Richard Jumar, Heiko Maass, Uwe Kühnapfel, Veit Hagenmeyer, Benjamin Schäfer
In the present paper, we utilize measurements of the power grid frequency obtained in European islands: the Faroe Islands, Ireland, the Balearic Islands and Iceland and investigate how their frequency can be predicted, compared to the Nordic power system, acting as a reference.
1 code implementation • 12 Apr 2022 • Rebecca Bauer, Tillmann Mühlpfordt, Nicole Ludwig, Veit Hagenmeyer
One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs).
1 code implementation • 30 Mar 2022 • Xinliang Dai, Yichen Cai, Yuning Jiang, Veit Hagenmeyer
This new variant is characterized by using a reduced modelling method of the distributed AC PF problem, which is reformulated as a zero-residual least-squares problem with consensus constraints.
1 code implementation • 13 Mar 2022 • Matias Quintana, Till Stoeckmann, June Young Park, Marian Turowski, Veit Hagenmeyer, Clayton Miller
Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction.
no code implementations • 28 Feb 2022 • Hüseyin K. Çakmak, Luc Janecke, Veit Hagenmeyer
The increasing share of renewable energy sources on distribution grid level as well as the emerging active role of prosumers lead to both higher distribution grid utilization, and at the same time greater unpredictability of energy generation and consumption.
no code implementations • 26 Feb 2022 • Tessina H. Scholl, Veit Hagenmeyer, Lutz Gröll
A core insight is that the Lyapunov-Krasovskii theorem resembles a theorem for Lyapunov-Rumyantsev partial stability in ODEs.
no code implementations • 3 Feb 2022 • Stefan Meisenbacher, Marian Turowski, Kaleb Phipps, Martin Rätz, Dirk Müller, Veit Hagenmeyer, Ralf Mikut
We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting.
1 code implementation • 17 Nov 2021 • Oliver Neumann, Nicole Ludwig, Marian Turowski, Benedikt Heidrich, Veit Hagenmeyer, Ralf Mikut
In the present paper, we analyze the impact of data representations on the performance of Deep Neural Networks using energy time series forecasting.
no code implementations • 26 Oct 2021 • Stefan Meisenbacher, Janik Pinter, Tim Martin, Veit Hagenmeyer, Ralf Mikut
Forecasts are elementary for sector coupling, where energy-consuming sectors are interconnected with the power-generating sector to address electricity storage challenges by adding flexibility to the power system.
1 code implementation • 18 Jun 2021 • Benedikt Heidrich, Andreas Bartschat, Marian Turowski, Oliver Neumann, Kaleb Phipps, Stefan Meisenbacher, Kai Schmieder, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer
Time series data are fundamental for a variety of applications, ranging from financial markets to energy systems.
no code implementations • 22 Jan 2021 • Martha Maria Frysztacki, Jonas Hörsch, Veit Hagenmeyer, Tom Brown
If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with better capacity factors and thus reduces system costs by up to 10% compared to a low resolution model.
Physics and Society Computation
1 code implementation • 5 Jan 2021 • Moritz Weber, Marian Turowski, Hüseyin K. Çakmak, Ralf Mikut, Uwe Kühnapfel, Veit Hagenmeyer
The CPI method copies data blocks with similar properties and pastes them into gaps of the time series while preserving the total energy of each gap.
no code implementations • 3 Dec 2020 • Elisabeth Zeyen, Veit Hagenmeyer, Tom Brown
Space and water heating accounts for about 40% of final energy consumption in the European Union and thus plays a key role in reducing overall costs and greenhouse gas emissions.
Physics and Society
2 code implementations • 20 Nov 2020 • Tillmann Mühlpfordt, Xinliang Dai, Alexander Engelmann, Veit Hagenmeyer
Solving the power flow problem in a distributed fashion empowers different grid operators to compute the overall grid state without having to share grid models-this is a practical problem to which industry does not have off-the-shelf answers.
Optimization and Control Systems and Control Systems and Control
no code implementations • 1 Nov 2020 • Friedrich Wiegel, Edoardo De Din, Antonello Monti, Klaus Wehrle, Marc Hiller, Martina Zitterbart, Veit Hagenmeyer
By means of a DC grid example, simulation results show the performance and applicability of the proposed novel receiver for packet-based energy dispatching.
no code implementations • 23 Sep 2020 • Karl Schwenk, Stefan Meisenbacher, Benjamin Briegel, Tim Harr, Veit Hagenmeyer, Ralf Mikut
Smart charging of Electric Vehicles (EVs) reduces operating costs, allows more sustainable battery usage, and promotes the rise of electric mobility.
no code implementations • 2 Jun 2020 • Richard Jumar, Heiko Maaß, Benjamin Schäfer, Leonardo Rydin Gorjão, Veit Hagenmeyer
Data were collected using a self-developed measurement instrument, the Electrical Data Recorder (EDR), connected mostly to conventional power sockets.
1 code implementation • 8 Apr 2020 • Tillmann Mühlpfordt, Frederik Zahn, Veit Hagenmeyer, Timm Faulwasser
With PolyChaos we provide a Julia software package that delivers the desired functionality: given a probability density function, PolyChaos offers several numerical routines to construct the respective orthogonal polynomials, and the quadrature rules together with tensorized scalar products.
Systems and Control Numerical Analysis Systems and Control Numerical Analysis Optimization and Control
no code implementations • 18 Feb 2020 • Hatem Khalloof, Wilfried Jakob, Shadi Shahoud, Clemens Duepmeier, Veit Hagenmeyer
The new method provides cluster or cloud parallelizability and is able to deal with a comparably large number of distributed energy resources.
no code implementations • 18 Mar 2019 • Jorge Ángel González Ordiano, Lutz Gröll, Ralf Mikut, Veit Hagenmeyer
Parametric quantile regressions are a useful tool for creating probabilistic energy forecasts.