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Data-driven copy-paste imputation for energy time series

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arXiv 2021年

作者： Weber, Moritz Turowski, Marian Çakmak, Hüseyin K. Mikut, Ralf Kühnapfel, Uwe Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analysis, and load management. Unfortunately, these time series are often characterized by missing values that must be handled before the data can be used. A common approach to handle missing values in time series is imputation. However, existing imputation methods are designed for power time series and do not take into account the total energy of gaps, resulting in jumps or constant shifts when imputing energy time series. In order to overcome these issues, the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. 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. The new method is evaluated on a real-world dataset that contains six shares of artificially inserted missing values between 1 and 30%. It outperforms by far the three benchmark imputation methods selected for comparison. The comparison furthermore shows that the CPI method uses matching patterns and preserves the total energy of each gap while requiring only a moderate run-time. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

CyPhERS: A Cyber-Physical Event Reasoning System providing real-time situational awareness for attack and fault response

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arXiv 2023年

作者： Müller, Nils Bao, Kaibin Matthes, Jörg Heussen, Kai Wind and Energy Systems Department Technical University of Denmark Building 330 Risø campus Roskilde4000 Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Building 445 Campus North Eggenstein-Leopoldshafen76344 Germany

Cyber-physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged by the complex interdependency of numerous physical and digital components, requiring to take cyber attacks and physical failures equally into account. The online event identification problem is further complicated through the lack of historical observations of critical but rare events, and the continuous evolution of cyber attack strategies. This work introduces and demonstrates CyPhERS, a Cyber-Physical Event Reasoning System. CyPhERS provides real-time information pertaining the occurrence, location, physical impact, and root cause of potentially critical events in CPSs, without the need for historical event observations. Key novelty of CyPhERS is the capability to generate informative and interpretable event signatures of known and unknown types of both cyber attacks and physical failures. The concept is evaluated and bench-marked on a demonstration case that comprises a multitude of attack and fault events targeting various components of a CPS. The results demonstrate that the event signatures provide relevant and inferable information on both known and unknown event types. © 2023, CC BY.

关键词： Cyber Physical System

Cyber-Physical Event Reasoning for Distributed Energy Resources on the Case of a Pv-Battery System

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SSRN

SSRN 2023年

作者： Müller, Nils Bao, Kaibin Heussen, Kai Wind and Energy Systems Department Technical University of Denmark Building 330 Risø campus Roskilde4000 Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Campus North Building 445 Eggenstein-Leopoldshafen76344 Germany

The widespread adoption of internet-connected and remotely controllable solar plants, battery storages, and other electric devices renders coordinated cyber-physical attacks against such distributed energy resources (DERs) an emerging risk for power systems. Effective incident response can be facilitated by online DER monitoring providing real-time information on event root causes and physical impacts. Such online event identification is challenged by the lack of historical attack observations, and emergence of new attack strategies. The cyber-physical event reasoning system CyPhERS provides real-time information on both known and unknown attack types in form of informative and interpretable event signatures, without need for historical attack samples. However, CyPhERS has been demonstrated on a generic cyber-physical laboratory testbed, considering human evaluation of event signatures. This work extends applicability of CyPhERS to DER monitoring on the case of a real PV-battery system affected by several cyber and cyber-physical attack types. In this context, an automated signature evaluation system is realized, and the concept adapted to specificities of DERs, such as weather and consumer-induced volatility. The results demonstrate that CyPhERS can be applied for online DER monitoring, providing information on root causes and physical impacts of both known attack strategies and unknown event types in an automated fashion. © 2023, The Authors. All rights reserved.

关键词： Energy resources

Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs

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Probabilistic Forecasting of Day-Ahead Electricity Prices an...

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Julius Trebbien Sebastian Pütz Benjamin Schäfer Heidi S. Nygård Leonardo Rydin Gorjão Dirk Witthaut Institute of Energy and Climate Research (IEK-10) Forschungszentrum Jülich Jülich Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Faculty of Science and Technology Norwegian University of Life Sciences Ås Norway

Accurate forecasts of electricity prices are crucial for the management of electric power systems and the development of smart applications. European electricity prices have risen substantially and became highly volatile after the Russian invasion of Ukraine, challenging established forecasting methods. Here, we present a Long Short-Term Memory (LSTM) model for the German-Luxembourg day-ahead electricity prices addressing these challenges. The recurrent structure of the LSTM allows the model to adapt to trends, while the joint prediction of both mean and standard deviation enables a probabilistic prediction. Using a physics-inspired approach–superstatistics–to derive an explanation for the statistics of prices, we show that the LSTM model faithfully reproduces both prices and their volatility.

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Inverse methods: How feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution?

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arXiv 2022年

作者： Frysztacki, Martha Maria Hagenmeyer, Veit Brown, Tom Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Institute of Energy Technology Technical University Berlin Einsteinufer 25 Berlin10587 Germany

Spatially highly-resolved capacity expansion models are often simplified to a lower spatial resolution because they are computationally intensive. The simplification mixes sites with different renewable features while ignoring transmission lines that can cause congestion. As a consequence, the results may represent an infeasible system when the capacities are fed back at higher spatial detail. Thus far there has been no detailed investigation of how to disaggregate results and whether the spatially highly-resolved disaggregated model is feasible. This is challenging since there is no unique way to invert the clustering. This article is split into two parts to tackle these challenges. First, methods to disaggregate spatially low-resolved results are presented: (a) an uniform distribution of regional results across its original highly-resolved regions, (b) a re-optimisation for each region separately, (c) an approach that minimises the "excess electricity". Second, the resulting highly-resolved models’ feasibility is investigated by running an operational dispatch. While re-optimising yields the best results, the third inverse method provides comparable results for less computational effort. Feasibility-wise, the study design strengthens that modelling countries by single regions is insufficient. State-of-the-art reduced models with 100–200 regions for Europe still yield 3%–7% of load-shedding, depending on model resolution and inverse method. © 2022, CC BY.

关键词： Inverse problems

Heterogeneous dual-frequency iot network for vital data acquisition 7th

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Heterogeneous dual-frequency iot network for vital data acqu...

7th KES International Conference on Sustainable Design and Manufacturing, KES-SDM 2020

作者： Salem, Mahmoud El-Maddah, Islam Youssef, Khaled Elkaseer, Ahmed Scholz, Steffen Mohamed, Hoda Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Navigation Science and Space Technology BSU Beni-Suef Egypt Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Faculty of Engineering Port Said University Port Said42526 Egypt

ISBN: (纸本)9789811581304

The IoT solutions for health informatics are suffering from restriction of using one frequency which makes the network is unable to communicate via different frequencies. Also, this limitation of using a single frequency fixes a single communication range between nodes without the capability to vary the covering rang. This could consume high power if the system has to transmit data on long distance due to the need to use signal repeater node/s. This paper reports the development of a new heterogeneous design of IoT network via unsymmetrical frequencies in order to sense, process, exchange and visualize two vital signs. The sensing and processing phases are performed via a convenient design of noninvasive vital data acquisition nods. These data acquisition nodes of vital signs are integrated into an embedded system with the IoT nodes in order to provide effective, accurate and real-time health monitoring for vital signs. The design of IoT broker node enables real-time analysis and concatenation of two vital signs. In addition, the broker facilitates the communication between nodes to each other over multi-frequencies in order to cover variant distances and thus to optimize the usage of power. Moreover, the proposed software of the IoT broker is able to address run-time issues of network nodes in real time without the need to reset the whole network. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Data acquisition

Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics

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arXiv 2024年

作者： Seiffarth, Johannes Blöbaum, Luisa Paul, Richard D. Friederich, Nils Yamachui Sitcheu, Angelo Jovin Mikut, Ralf Scharr, Hanno Grünberger, Alexander Nöh, Katharina Institute of Bio- and Geosciences IBG-1: Biotechnology Forschungszentrum Jülich GmbH Jülich Germany RWTH Aachen University Aachen Germany Multiscale Bioengineering Bielefeld University Bielefeld Germany Institute for Advanced Simulation IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich GmbH Jülich Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Institute of Biological and Chemical Systems Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Microsystems in Bioprocess Engineering Karlsruhe Institute of Technology Karlsruhe Germany

Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnological applications. In microbial live-cell imaging (MLCI), a few to thousands of cells have to be detected and tracked within dozens of growing cell colonies. The challenge of tracking cells is heavily influenced by the experiment parameters, namely the imaging interval and maximal cell number. For now, tracking benchmarks are not widely available in MLCI and the effect of these parameters on the tracking performance are not yet known. Therefore, we present the largest publicly available and annotated dataset for MLCI, containing more than 1.4 million cell instances, 29k cell tracks, and 14k cell divisions. With this dataset at hand, we generalize existing tracking metrics to incorporate relevant imaging and experiment parameters into experiment-aware metrics. These metrics reveal that current cell tracking methods crucially depend on the choice of the experiment parameters, where their performance deteriorates at high imaging intervals and large cell colonies. Thus, our new benchmark quantifies the influence of experiment parameters on the tracking quality, and gives the opportunity to develop new data-driven methods that generalize across imaging and experiment parameters. The benchmark dataset is publicly available at https://***/doi/10.5281/zenodo.7260136. © 2024, CC BY.

关键词： Cytology

Non-linear, bivariate stochastic modelling of power-grid frequency applied to islands

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Non-linear, bivariate stochastic modelling of power-grid fre...

Power Tech Conference

作者： Ulrich Oberhofer Leonardo Rydin Gorjao G. Cigdem Yalcin Oliver Kamps Veit Hagenmeyer Benjamin Schafer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Faculty of Science and Technology Norwegian University of Life Sciences As Norway Department of Physics Istanbul University Istanbul Turkey Center for Nonlinear Sciences (CeNoS) University of Munster Germany

Mitigating climate change requires a transition away from fossil fuels towards renewable energy. As a result, power generation becomes more volatile and options for microgrids and islanded power-grid operation are being broadly discussed. Therefore, studying the power grids of physical islands, as a model for islanded microgrids, is of particular interest when it comes to enhancing our understanding of power-grid stability. In the present paper, we investigate the statistical properties of the power-grid frequency of three island systems: Iceland, Ireland, and the Balearic Islands. We utilise a Fokker-Planck approach to construct stochastic differential equations that describe market activities, control, and noise acting on power-grid dynamics. Using the obtained parameters we create synthetic time series of the frequency dynamics and compare them to empirical data. Our main contribution is to propose two extensions of stochastic power-grid frequency models and showcase the applicability of these new models to non-Gaussian statistics, as encountered in islands.

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Improving 3D deep learning segmentation with biophysically motivated cell synthesis

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arXiv 2024年

作者： Bruch, Roman Vitacolonna, Mario Nürnberg, Elina Sauer, Simeon Rudolf, Rüdiger Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Institute of Molecular and Cell Biology Mannheim University of Applied Sciences Paul-Wittsack-Straße 10 Mannheim68163 Germany CeMOS Mannheim University of Applied Sciences Paul-Wittsack-Straße 10 Mannheim68163 Germany CHARISMA Mannheim University of Applied Sciences Paul-Wittsack-Straße 10 Mannheim68163 Germany

Biomedical research increasingly relies on 3D cell culture models and AI-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standard for ground truth data, is too time-consuming and thus not feasible for the generation of large 3D training datasets. To address this, we present a novel framework for generating 3D training data, which integrates biophysical modeling for realistic cell shape and alignment. Our approach allows the in silico generation of coherent membrane and nuclei signals, that enable the training of segmentation models utilizing both channels for improved performance. Furthermore, we present a new GAN training scheme that generates not only image data but also matching labels. Quantitative evaluation shows superior performance of biophysical motivated synthetic training data, even outperforming manual annotation and pretrained models. This underscores the potential of incorporating biophysical modeling for enhancing synthetic training data quality. Copyright © 2024, The Authors. All rights reserved.

关键词： Cell culture