TY - JOUR A1 - Münch, Justin A1 - Priesmann, Jan A1 - Reich, Marius A1 - Tillmanns, Marius A1 - Praktiknjo, Aaron A1 - Adam, Mario T1 - Uplifting the Complexity of Analysis for Probabilistic Security of Electricity Supply Assessments using Artificial Neural Networks JF - Energy and AI N2 - The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 minutes to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time. KW - Security of electricity supply KW - Probabilistic simulation KW - Metamodeling KW - Artificial neural networks KW - Regression KW - HSD Publikationsfonds KW - DFG Publikationskosten KW - KIVi Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-45359 SN - 2666-5468 N1 - Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 532148125 and supported by the central publication fund of Hochschule Düsseldorf University of Applied Sciences. PB - Elsevier ER - TY - JOUR A1 - Priesmann, Jan A1 - Münch, Justin A1 - Tillmanns, Marius A1 - Ridha, E. A1 - Spiegel, Thomas A1 - Reich, Marius A1 - Adam, Mario A1 - Nolting, L. A1 - Praktiknjo, Aaron T1 - Artificial intelligence and design of experiments for resource adequacy assessment in power systems JF - Energy Strategy Reviews KW - KiVi KW - Design of experiments KW - Resource adequacy KW - Security of supply KW - Artificial intelligence KW - KIVi Y1 - 2024 U6 - https://doi.org/10.1016/j.esr.2024.101368 SN - 2211-467X VL - 53 PB - Elsevier ER - TY - GEN A1 - Gottschald, Jonas A1 - Reich, Marius A1 - Adam, Mario T1 - Lernende Algorithmen - Künstliche Intelligenz für Wärmenetze T2 - hn21 Journal KW - BestHeatNet Y1 - 2021 UR - https://www.hn-nrw.de/aktivitaeten/publikationen/journal/# IS - 2021 SP - 8 EP - 8 PB - Zentrum für Forschungskommunikation CY - Köln ER - TY - GEN A1 - Lambach, Stefan A1 - Kowalski, M. A1 - Reich, Marius A1 - Adam, Mario T1 - Vermessung und Modellierung eines Komfort-Lüftungsgerätes mit passiver Wärmerückgewinnung und aktiver, umschaltbarer Luft/Luft-Wärmepumpe im Kühlbetrieb T2 - KI Kälte Luft Klimatechnik KW - Coolplan-AIR Y1 - 2020 SN - 1865-5432 VL - 56 IS - 10 SP - 48 EP - 55 PB - Hüthig ER - TY - CHAP A1 - Reich, Marius A1 - Adam, Mario A1 - Lambach, Stefan T1 - Comparison of different Methods for Approximating Models of Energy Supply Systems and Polyoptimising the Systems-Structure and Components-Dimension T2 - 30th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2017), San Diego, California, USA, 2-6 July 2017 KW - Fast-Energy-Design Y1 - 2018 SN - 9781510862562 SP - 2921 EP - 2933 PB - Curran Associates Inc CY - Red Hook, NY ER - TY - JOUR A1 - Nolting, Lars A1 - Spiegel, Thomas A1 - Reich, Marius A1 - Adam, Mario A1 - Praktiknjo, Aaron T1 - Can energy system modeling benefit from artificial neural networks? Application of two-stage metamodels to reduce computation of security of supply assessments JF - Computers & Industrial Engineering KW - KIVi Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-26979 SN - 0360-8352 VL - 142 PB - Elsevier ER - TY - JOUR A1 - Biedermann, Till M. A1 - Reich, Marius A1 - Kameier, Frank A1 - Adam, Mario T1 - Assessment of statistical sampling methods and approximation models applied to aeroacoustic and vibroacoustic problems JF - Advances in Aircraft and Spacecraft Science KW - artificial neural networks KW - design of experiments KW - Latin hypercube sampling KW - aeroacoustics KW - aerodynamics KW - spectral analysis Y1 - 2019 UR - http://www.techno-press.org/content/?page=article&journal=aas&volume=6&num=6&ordernum=7 U6 - https://doi.org/10.12989/aas.2019.6.6.531 SN - 2287-528X VL - 6 IS - 6 SP - 529 EP - 550 PB - Techno Press ER - TY - CHAP A1 - Gottschald, Jonas A1 - Reich, Marius A1 - Adam, Mario A1 - Leibauer, R. T1 - Selbstlernende Betriebsoptimierung einer hybriden Nahwärmeversorgung T2 - Fachkonferenz Digitalisieren - Sektoren koppeln - Flexibilisieren, 24.11.2020, Berlin KW - BestHeatNet Y1 - 2020 CY - Berlin ER - TY - JOUR A1 - Reich, Marius A1 - Gottschald, Jonas A1 - Riegebauer, Philipp A1 - Adam, Mario T1 - Predictive Control of District Heating System Using Multi-Stage Nonlinear Approximation with Selective Memory JF - Energies KW - BestHeatNet KW - DOAJ Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-33627 SN - 1996-1073 VL - 13 IS - 24 PB - MDPI ER - TY - CHAP A1 - Adam, Mario A1 - Gottschald, Jonas A1 - Reich, Marius A1 - Götzelmann, Dennis T1 - BestHeatNet – Selbstlernende Betriebsoptimierung einer hybriden Nahwärmeversorgung T2 - Der Geothermiekongress, 19.-21.11.2019, München KW - BestHeatNet Y1 - 2019 UR - https://www.der-geothermiekongress.de/fileadmin/user_upload/DGK/DGK_2019/Teilnahme/P12_Gottschald_BestHeatNet-124.pdf CY - München ER -