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Data analytics in the electricity sector – A quantitative and qualitative literature review

Energy and AI 2020年第1期1卷 145-167页

作者： Frederik vom Scheidt Hana Medinova Nicole Ludwig Bent Richter Philipp Staudt Christof Weinhardt Institute for Information Systems and Marketing Karlsruhe Institute of TechnologyKaiserstr.8976133 KarlsruheGermany Institute for Automation and Applied Informatics Karlsruhe Institute of TechnologyHermann-von-Helmholtz-Platz 176344 Eggenstein-LeopoldshafenGermany

The rapid transformation of the electricity sector increases both the opportunities and the need for Data *** recent years,various new methods and fields of application have been *** research is growing and becoming more diverse and specialized,it is essential to integrate and structure the fragmented body of scientific *** therefore conduct a systematic review of studies concerned with developing and applying Data Analytics methods in the context of the electricity value ***,we provide a quantitative high-level overview of the status quo of Data Analytics research,and show historical literature growth,leading countries in the field and the most intensive international ***,we qualitatively review over 200 high-impact studies to present an in-depth analysis of the most prominent applications of Data Analytics in each of the electricity sector’s areas:generation,trading,transmission,distribution,and *** each area,we review the state-of-the-art Data Analytics applications and *** addition,we discuss used data sets,feature selection methods,benchmark methods,evaluation metrics,and model complexity and run *** the findings from the different areas,we identify best practices and what researchers in one area can learn from other ***,we highlight potential for future research.

关键词： Data analytics Electricity Machine learning Generation Price Transmission Distribution Consumption

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A Fused Machine Learning Approach for Intrusion Detection System

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Computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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An Evaluation of Graph Algorithms for the Wind Farm Cable Layout Problem under Electrical Aspects

An Evaluation of Graph Algorithms for the Wind Farm Cable La...

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International Universities Power Engineering Conference (UPEC)

作者： Sascha Gritzbach Hüseyin Çakmak Pascal Mehnert Torsten Ueckerdt Veit Hagenmeyer Institute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Karlsruhe Institute of Technology Karlsruhe Germany

The task of the Wind Farm Cable Layout Problem is to design a cable system between turbines and substations such that all turbine output can be transmitted to the substations. This problem can be modelled with different levels of complexity. While a higher level of complexity yields solutions that can be implemented in a real-world setting more readily, problem instances also become more difficult to solve or even remain intractable. More simplistic models are easier to solve but their usability could be inhibited. One such more simplistic model for installation cost minimization contains a network flow and a suitable minimum-cost flow algorithm provides good cable layouts on instances with up to 500 turbines within tens of seconds. The question remains whether those cable layouts are suitable for electrical implementation as well. We propose a workflow to evaluate the cable layouts generated from such algorithms under electrical aspects. This workflow converts the output of cable layout optimization algorithms to power flow models. The power flow models are simulated using the simulation framework eASiMOV. The evaluation of the power flow simulations under electrical metrics shows that output from the minimum-cost flow algorithm and from an approach solving a Mixed-Integer Linear Program perform very well under electrical aspects on a vast majority of input instances. For the remaining minority we are able to identify structures in the solutions that result in a worse performance. These observations can be used by the algorithm engineers as possible directions for future improvements.

关键词： Substations Power cables Layout Software algorithms Wind farms Power system stability Software

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4.3 towards classification of technical sound events with deep learning models

4.3 towards classification of technical sound events with de...

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2019 Knowledge Management in Digital Work Environments State-of-the-Art and Outlook, WM 2019

作者： Rieder, Constantin Germann, Markus Scherer, Klaus Peter Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Ibidem

ISBN: (纸本)9783885796978

Sounds of machines and mechanical systems contain a lot of information about the observed object and its state. Experienced engineers and technical service staff can often identify or classify a certain technical object with state via its sound. An equivalent automated system with such capabilities is difficult to realise because of noisy unknown surroundings. In this paper, we show an approach to implement the mentioned characteristics with deep learning methods and enhance the power of a technical assistance system. © 2020 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Deep learning

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Review of automated time series forecasting pipelines

arXiv

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arXiv 2022年

作者： Meisenbacher, Stefan Turowski, Marian Phipps, Kaleb Rätz, Martin Müller, Dirk Hagenmeyer, Veit Mikut, Ralf Institute For Automation And Applied Informatics Karlsruhe Institute Of Technology Eggenstein-Leopoldshafen76344 Germany Institute For Energy Efficient Buildings And Indoor Climate RWTH Aachen University Aachen52062 Germany Jülich52425 Germany

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. 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. © 2022, CC BY.

关键词： Pipelines

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AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy

arXiv

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arXiv 2023年

作者： Friederich, Nils Sitcheu, Angelo Yamachui Neumann, Oliver Eroğlu-Kayıkçı, Süheyla Prizak, Roshan Hilbert, Lennart Mikut, Ralf Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Institute of Biological and Chemical Systems Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Zoological Institute Karlsruhe Institute of Technology Fritz-Haber-Weg 4 Karlsruhe76131 Germany

In the biomedical environment, experiments assessing dynamic processes are primarily performed by a human acquisition supervisor. Contemporary implementations of such experiments frequently aim to acquire a maximum number of relevant events from sometimes several hundred parallel, non-synchronous processes. Since in some high-throughput experiments, only one or a few instances of a given process can be observed simultaneously, a strategy for planning and executing an efficient acquisition paradigm is essential. To address this problem, we present two new methods in this paper. The first method, Encoded Dynamic Process (EDP), is Artificial Intelligence (AI)-based and represents dynamic processes so as to allow prediction of pseudo-time values from single still images. Second, with Experiment automation Pipeline for Dynamic Processes (EAPDP), we present a Machine Learning Operations (MLOps)-based pipeline that uses the extracted knowledge from EDP to efficiently schedule acquisition in biomedical experiments for dynamic processes in practice. In a first experiment, we show that the pre-trained State-Of-The-Art (SOTA) object segmentation method Contour Proposal Networks (CPN) works reliably as a module of EAPDP to extract the relevant object for EDP from the acquired three-dimensional image stack. © 2023, CC BY.

关键词： Pipelines

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Physics-inspired machine learning for power grid frequency modelling

arXiv

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arXiv 2022年

作者： Kruse, Johannes Cramer, Eike Schäfer, Benjamin Witthaut, Dirk Jülich52428 Germany Institute for Theoretical Physics University of Cologne Köln50937 Germany Jülich52428 Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a non-autonomous system whose parameters vary strongly with time. However, the external driving factors are usually only available on coarse scales and the actual dependencies of the dynamic system parameters are generally unknown. Here, we propose a physics-inspired machine learning model that bridges the gap between large-scale drivers and short-term dynamics of the power system. Integrating stochastic differential equations and artificial neural networks, we construct a probabilistic model of the power grid frequency dynamics in Continental Europe. Its probabilistic prediction outperforms the daily average profile, which is an important benchmark. Using the integrated model, we identify and explain the parameters of the dynamical system from the data, which reveals their strong time-dependence and their relation to external drivers such as wind power feed-in and fast generation ramps. Finally, we generate synthetic time series from the model, which successfully reproduce central characteristics of the grid frequency such as their heavy-tailed distribution. All in all, our work emphasises the importance of modelling power system dynamics as a stochastic non-autonomous system with both intrinsic dynamics and external drivers. Copyright © 2022, The Authors. All rights reserved.

关键词： Wind power

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*** — A Julia Package for Polynomial Chaos in Systems and Control

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IFAC-PapersOnLine 2020年第2期53卷 7210-7216页

作者： Tillmann Mühlpfordt Frederik Zahn Veit Hagenmeyer Timm Faulwasser Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

Polynomial chaos expansion (pce) is an increasingly popular technique for uncertainty propagation and quantification in systems and control. Based on the theory of Hilbert spaces and orthogonal polynomials, PCE allows for a unifying mathematical framework to study systems under arbitrary uncertainties of finite variance; we introduce this problem as a so-called mapping under uncertainty. For practical PCE-based applications we require orthogonal polynomials relative to given probability densities, and their quadrature rules. With *** we provide a Julia software package that delivers the desired functionality: given a probability density function, *** offers several numerical routines to construct the respective orthogonal polynomials, and the quadrature rules together with tensorized scalar products. *** is the first PCE-related software written in Julia, a scientific programming language that combines the readability of scripted languages with the speed of compiled languages. We provide illustrating numerical examples that show both PCE and *** in action.

关键词： polynomial chaos expansion uncertainties stochastic optimal control Julia

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The Proposal of Customized Convolutional Neural Network Using for Image Blur Recognition 4th

The Proposal of Customized Convolutional Neural Network Usin...

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4th Computational Methods in Systems and Software, CoMeSySo 2020

作者： Borkin, Dmitrii Nemeth, Martin Nemethova, Andrea Faculty of Materials Science and Technology in Trnava Institute of Applied Informatics Automation and Mechatronics Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (纸本)9783030633219

This paper describes one of the parts of our research topic devoted to the image recognition and computer vision. In the field of automation, it is not new, that industry lines are under controls of camera systems. It is necessary to have a clear and visible image for automatic detection of various damages, fails, alarms, objects and right in time control. We divided our research into several stages. In the first stage, we are dealing with the recognition of the blur in the image. We are dealing with classification problem whether the image is blured or in focus. In the initial steps of our research, we have created a dataset of images, which is divided into four categories. In this paper we describe suitable methods to solve this task, we discuss the pros and cons and we also propose our own classification model based on artificial neural networks. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Devices control and monitoring on the production level using wonderware platform 9th

Devices control and monitoring on the production level using...

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9th Computer Science On-line Conference, CSOC 2020

作者： Borkin, Dmitrii Barton, Martin Nemeth, Martin Tanuska, Pavol Faculty of Materials Science and Technology in Trnava Institute of Applied Informatics Automation and Mechatronics Slovak University of Technology in Bratislava Trnava Slovakia

ISBN: (纸本)9783030519735

In our paper we present the method for cross-platform control and monitoring of a device on a production level. Hardware and software inconsistency makes it often difficult to build a consistent and robust control and monitoring environment for a production process. For the purpose of demonstrating this inconsistence and application of our method, we have chose a motor with the frequency converter made by Siemens and the Wonderware platform as the supervisory control and monitoring software environment. In this paper we present the results in a form of proposed hardware setup scheme and software application for supervisory control and monitoring of the device, developed in the Wonderware environment. © Springer Nature Switzerland AG 2020.

关键词： Robust control

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