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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Kastner, Adam Gröll, Lutz Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350311402

Averaging methods are widely used for the control-oriented modeling of systems where high-frequency actuation is used to steer the average of the control variables, such as switching power converters or mechanical systems with vibrational control. Higher-order averaging provides approximations for the trajectories with improved accuracy compared to the commonly used first-order averaging method. Furthermore, differential flatness is a property of dynamical systems which allows parametrizing state and input trajectories from a flat output without integration. In this contribution we investigate the relation between higher-order averaging and flatness via examples of electrical and mechanical systems. © 2023 IEEE.

关键词： Dynamical systems

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A comparison of clustering methods for the spatial reduction of renewable electricity optimisation models of Europe

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Energy informatics 2022年第1期5卷 1-28页

作者： Frysztacki, Martha Maria Recht, Gereon Brown, Tom Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Institute of Energy Technology Technical University of Berlin Berlin Germany

Modeling the optimal design of the future European energy system involves large data volumes and many mathematical constraints, typically resulting in a significant computational burden. As a result, modelers often apply reductions to their model that can have a significant effect on the accuracy of their results. This study investigates methods for spatially clustering electricity system models at transmission level to overcome the computational constraints. Spatial reduction has a strong effect both on flows in the electricity transmission network and on the way wind and solar generators are aggregated. Clustering methods applied in the literature are typically oriented either towards preserving network flows or towards preserving the properties of renewables, but both are important for future energy systems. In this work we adapt clustering algorithms to accurately represent both networks and renewables. To this end we focus on hierarchical clustering, since it preserves the topology of the transmission system. We test improvements to the similarity metrics used in the clustering by evaluating the resulting regions with measures on renewable feed-in and electrical distance between nodes. Then, the models are optimised under a brownfield capacity expansion for the European electricity system for varying spatial resolutions and renewable penetration. Results are compared to each other and to existing clustering approaches in the literature and evaluated on the preciseness of siting renewable capacity and the estimation of power flows. We find that any of the considered methods perform better than the commonly used approach of clustering by country boundaries and that any of the hierarchical methods yield better estimates than the established method of clustering with k-means on the coordinates of the network with respect to the studied parameters. © 2022, The Author(s).

关键词： K-means clustering

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Using Large Language Models for Extracting Structured Information From Scientific Texts

Using Large Language Models for Extracting Structured Inform...

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作者： Rettenberger, Luca Münker, Marc F. Schutera, Mark Niemeyer, Christof M. Rabe, Kersten S. Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Karlsruhe Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Institute for Biological Interfaces Karlsruhe Institute of Technology Germany

Extracting structured information from scientific works is challenging as sought parameters or properties are often scattered across lengthy texts. We introduce a novel iterative approach using Large Language Models (LLMs) to automate this process. Our method first condenses scientific literature, preserving essential information in a dense format, then retrieves predefined attributes. As a biomedical application example, our concept is employed to extract experimental parameters for preparing Metal-Organic Frameworks (MOFs) from scientific work to enable complex and information-rich applications in the biotechnology-oriented life sciences. Our open-source method automates extracting information from verbose texts, converting them into structured and easily navigable data. This considerably improves scientific literature research by utilizing the power of LLMs and paves the way for enhanced and faster information extraction from extensive scientific texts. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Information retrieval

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Towards a Real-World Dispatchable Feeder 8

Towards a Real-World Dispatchable Feeder

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8th IEEE Workshop on the Electronic Grid, eGRID 2023

作者： Beichter, Sebastian Beichter, Maximilian Werling, Dorina Galenzowski, Johannes Weise, Victor Hildenbrand, Christoph Wiegel, Friedrich Mikut, Ralf Waczowicz, Simon Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany

ISBN: (纸本)9798350327007

With the integration of renewable energy sources, electric vehicles, and batteries at the low-voltage distribution grid level, residential buildings are increasingly evolving from consumers to prosumers. As the prosumers burden the distribution grid with their uncertain interactions, the need for solutions to counteract this uncertainty becomes more important. One such solution is schedule-based control strategies, such as the dispatchable feeder. Thereby, the dispatchable feeder uses intelligent managed flexible components such as battery energy storage system (BESS) and controllable loads to follow a committed schedule. While the concept of the dispatchable feeder is widely discussed in the literature, the practical challenges associated with its real-world implementation remain under-explored. Therefore, this paper identifies challenges associated with the real-world implementation of the dispatchable feeder in residential buildings. Further, it discusses important future research questions regarding the concept and its valuable integration into the real world. It also proposes an experimental research infrastructure for systematically investigating the raised questions using the real-world laboratory of the Energy Lab 2.0. © 2023 IEEE.

关键词： Renewable energy

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Forecasting energy time series with profile neural networks 20

Forecasting energy time series with profile neural networks

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11th ACM International Conference on Future Energy Systems, e-Energy 2020

作者： Heidrich, Benedikt Turowski, Marian Ludwig, Nicole Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany

ISBN: (纸本)9781450380096

Forecasting the energy demand is essential for network operators to balance the grid, in particular with the increasing share of renewable energy sources. Neural networks, especially deep neural networks, have shown promising results in recent forecasting tasks. However, they often struggle learning periodicities in time series efficiently. In line with the finding that deep learning can be improved with statistical information, we introduce profile neural networks based on the fast and promising convolutional neural networks. The underlying idea of profile neural networks is that decomposing periodic energy time series into a standard load profile, a trend, and a colorful noise module improves the forecasting accuracy. The proposed deep neural network architecture is applied to real-world electricity data from buildings on a university campus, more specifically of one building with strong seasonal variation and one building with weak seasonal variation. The new architecture outperforms current state-of-the-art deep learning benchmark models regarding the forecasting accuracy on forecast horizons of one day and one week-ahead, improving the mean absolute scaled error by up to 25%, as well as regarding the trade-off between training time and accuracy. © 2020 Owner/Author.

关键词： Time series

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A fuzzy logic approach for economic energy-efficient heat pump operation in thermal building control

A fuzzy logic approach for economic energy-efficient heat pu...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Kovačević, Jovana Süß, Anne-Christin Çakmak, Hüseyin K. Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany

ISBN: (纸本)9789531842976

Advanced thermal control has great potential to decrease energy consumption in buildings. Thereby, encountering the balance between thermal comfort, and electricity load reduction is a challenge. For this, advanced control systems need to be developed that can respond to external signals such as electricity prices and are robust to different types of buildings to be widely utilized. In this paper, we propose and investigate two fuzzy control systems, an economic-energy-efficient controller and a comfort controller. The comfort controller focuses on thermal comfort, whereas the economic-energy-efficient controller is attentive to electricity prices and the energy efficiency of the heat pump. For the simulation, we use a reduced-order building model identified by measurements of a real-world building. Furthermore, we compare the proposed controllers with a hysteresis controller as a lower benchmark control strategy. Simulation results reveal that an economic-energy-efficient controller can reduce electricity consumption by 14.92% and costs by 18.41 % when compared to a benchmark controller and with mean thermal discomfort of 0.216 °C over an evaluation period of two months. The comfort controller has slightly smaller costs and electricity consumption than the hysteresis one, but less zone temperature fluctuations, enhancing the quality of controlled zone temperature and occupants' comfort. © 2024 IEEE.

关键词： Fuzzy control

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New Co-Simulation Variants for Emissions and Cost Reduction of Sustainable District Heating Planning 16

New Co-Simulation Variants for Emissions and Cost Reduction ...

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16th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2024

作者： Cheng, Haozhen Buccoliero, Verena Kocher, Alexander Hagenmeyer, Veit Çakmak, Huseyin K. Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9798350386127

Classical heating of residential areas is very energy-intensive, so alternatives are needed, including renewable energies and advanced heating technologies. Thus, the present paper introduces a new methodology for comprehensive variant analysis for future district heating planning, aiming at optimizing emissions and costs. For this, an extensive Modelica-based modelling study comprising models of heating center, heat grid pipelines and heating interface units to buildings are coupled in co-simulations. These enable a comparative analysis of the economic feasibility and sustainability for various technologies and energy carriers to be carried out. The new modular and highly parameterizable building model serves for validation of the introduced heat grid model. The results show that bio-methane as an energy source reduces carbon equivalent emissions by nearly 70% compared to conventional natural gas heating, and the use of hydrogen as an energy source reduces carbon equivalent emissions by 77% when equipped with a heat pump. In addition, the use of ground source heat pumps has a high economic viability when economic benefits are taken into account. The study findings highlight the importance of strategic planning and flexible design in the early stages of district development in order to achieve improved energy efficiency and a reduced carbon footprint. © 2024 IEEE.

关键词： Carbon capture and utilization

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Additive Manufacturing: Comparative Study of an IoT Integrated Approach and a Conventional Solution

Additive Manufacturing: Comparative Study of an IoT Integrat...

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Future Technologies Conference, FTC 2020

作者： Shandilya, Harshit Kuchta, Matthias Elkaseer, Ahmed Müller, Tobias Scholz, Steffen G. Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9783030630881

The domain of additive manufacturing is an ever-growing sphere of research that will soon make its mark in countless industrial applications. The prowess of additive manufacturing increases significantly when it is integrated along with the Internet of Things. Upon connecting a Prusa MK3S 3D printer to a Raspberry Pi 3B and operating it via Octoprint, the merits of IoT integration are assessed. Comparing the samples from an IoT integrated printer and a standard printer, a contrast is made between the print qualities of the test samples from the two methods of printing. Combining the additive manufacturing set up along with IoT, multiple additional functionalities are obtained, including remote monitoring, remote operation and lower computing requirements. The results of the experiments lead us to the conclusion that the setup can be operated with lesser computing power and also incorporate remote operation and monitoring while maintaining acceptable print quality. The loss in print quality from the IoT approach is negligible when juxtaposed with the prints from a standard desktop operated printer. © 2021, Springer Nature Switzerland AG.

关键词： 3D printers

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Transformer training strategies for forecasting multiple load time series

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Energy informatics 2023年第1期6卷 20页

作者： Hertel, Matthias Beichter, Maximilian Heidrich, Benedikt Neumann, Oliver Schäfer, Benjamin Mikut, Ralf Hagenmeyer, Veit Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the number of monitored clients will increase with the ongoing smart meter rollout, the amount of data per client will always be limited. 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. In experiments with two datasets containing load time series from several hundred clients, we find that the global training strategy is superior to the multivariate and local training strategies used in related work. On average, the global training strategy results in 21.8% and 12.8% lower forecasting errors than the two other strategies, measured across forecasting horizons from one day to one month into the future. A comparison to linear models, multi-layer perceptrons and LSTMs shows that Transformers are effective for load forecasting when they are trained with the global training strategy. © 2023, The Author(s).

关键词： Time series

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Gossen’s first law in the modeling for demand side management: a thorough heat pump case study with deep learning based partial time series data generation

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Energy informatics 2024年第1期7卷 47页

作者： Li, Chang Brecher, Gina Kovačević, Jovana Çakmak, Hüseyin K. Förderer, Kevin Matthes, Jörg Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany

Gossen’s First Law describes the law of diminishing marginal utility. This paper aims to further verify the proposed hypothesis that Gossen’s First Law also holds in the modeling for Demand Side Management (DSM) with a thorough heat pump case study. The proposed hypothesis states that in general the complexity-utility relationship in the field of DSM modeling could be represented by a diminishing marginal utility curve. On the other hand, in data based modeling, when utilizing a large dataset for validation, the data integrity is critical to the reliability of the results. However, the absence of partial time series data may occur during the measurement due to missing sensors or IT related issues. In this work, an extensive real-world open dataset of a ground source heat pump is utilized for the case study. In the raw data, one key variable namely the flow rate is missing. Thus, three different algorithms based on machine learning and deep learning architectures namely Random Forest (RF), Long Short-Term Memory (LSTM) and Transformer are applied to predict the flow rate by utilizing an open loop forecasting. The raw data are first pre-processed with a time interval of one hour and then used for training, validation and forecast. Furthermore, a modified persistence model as the baseline is also defined. The predicted flow rate using LSTM yields the lowest error of 7.47% nMAE and 10.56% nRMSE respectively. The forecast results are then utilized in the following step of modeling of a heat pump use case. With the introduced quantification method for complexity and a modified version for utility, we further verify the proposed hypothesis with a longer time horizon of 7 days. © The Author(s) 2024.

关键词： Forecasting

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