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 - Glembin, Jens A1 - Adam, Mario A1 - Deidert, Jörn A1 - Jagnow, Kati A1 - Rockendorf, Gunter A1 - Wirth, Hans Peter T1 - Simulation and Evaluation of Different Boiler Implementations and Configurations in Solar Thermal Combi Systems JF - Energy Procedia KW - Kessel und große Solarthermieanlagen Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-27350 N1 - Acknowledgements The work presented in this paper has been realized within the project “Integration von Heizkesseln in Wärmeverbundsysteme mit großen Solaranlagen“ (Integration of heating boilers in thermal energy distribution circuits with large solar systems), which has been funded by the German Ministry for Environment, Nature Conservation and Nu-clear Safety based on a decision of the German Federal Parliament (FKZ 0325958Z). Within the project, the authors developed a new boiler model for dynamic simulations and validated it against test rig and field data. Field measurements and additional system simulations have been used to analyze and evaluate the interaction of the solar thermal system and the boiler. The authors are grateful for the project support. The content of this publication is in the responsibility of the authors. VL - 30 SP - 601 EP - 610 PB - Elsevier ER - TY - JOUR A1 - Schramm, Sebastian A1 - Adam, Mario T1 - Storage in Solar Process Heat Applications JF - Energy Procedia KW - Solare-Prozesswärme-Standards Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-27244 SN - 1876-6102 N1 - PII: S1876610214003981 VL - 48 SP - 1202 EP - 1209 PB - Elsevier 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 - RPRT A1 - Adam, Mario A1 - Dreher, Martina A1 - Ebling, Dirk A1 - Knopp, Reinhold A1 - Neef, Matthias A1 - Niess, Robert A1 - Pohl, Tobias A1 - Stahl, Wilhelm A1 - van Rießen, Anne A1 - Waldow, Friederike A1 - Weber, Konradin A1 - Weis, Stephanie T1 - LUST Forschungsprojekt - lebenswerte und umweltgerechte Stadt N2 - LUST (lebenswerte und umweltgerechte Stadt) ist ein interdisziplinäres Projekt der Fachbereiche Architektur, Maschinenbau und Verfahrenstechnik sowie Kultur- und Sozialwissenschaften der Hochschule Düsseldorf. Mittelpunkt des Projektes ist die fachübergreifende Entwicklung neuer Konzepte für eine lebenswerte und umweltgerechte Stadt. Es wurde gefördert vom Land Nordrhein-Westfalen im Rahmen des Programms „FH Struktur“. N2 - LUST (Liveable and environment-friendly Urban SiTe) is an interdisciplinary project of the faculties of Architecture, Mechanical and Process Engineering as well as Social Studies and Cultural Sciences at the University of Applied Sciences Düsseldorf. The four-year (2013 – 2016) project is funded by the Ministry of Innovation, Science and Research of North Rhine–Westphalia. The aim is to develop holistic, new concepts for a liveable and environmentally friendly city in a multidisciplinary manner. KW - Stadtentwicklung KW - lebenswert KW - umweltgerecht KW - substainable urban development KW - environmentally friendly KW - liveable Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-11458 SN - 978-3-941334-43-4 CY - Düsseldorf ER - 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 -