@article{NoltingSpiegelReichetal.2020, author = {Nolting, Lars and Spiegel, Thomas and Reich, Marius and Adam, Mario and Praktiknjo, Aaron}, title = {Can energy system modeling benefit from artificial neural networks? Application of two-stage metamodels to reduce computation of security of supply assessments}, series = {Computers \& Industrial Engineering}, volume = {142}, journal = {Computers \& Industrial Engineering}, publisher = {Elsevier}, issn = {0360-8352}, doi = {10.1016/j.cie.2020.106334}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-26979}, year = {2020}, language = {en} } @article{GlembinAdamDeidertetal.2012, author = {Glembin, Jens and Adam, Mario and Deidert, J{\"o}rn and Jagnow, Kati and Rockendorf, Gunter and Wirth, Hans Peter}, title = {Simulation and Evaluation of Different Boiler Implementations and Configurations in Solar Thermal Combi Systems}, series = {Energy Procedia}, volume = {30}, journal = {Energy Procedia}, publisher = {Elsevier}, doi = {10.1016/j.egypro.2012.11.070}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-27350}, pages = {601 -- 610}, year = {2012}, language = {en} } @article{BiedermannReichKameieretal.2019, author = {Biedermann, Till M. and Reich, Marius and Kameier, Frank and Adam, Mario}, title = {Assessment of statistical sampling methods and approximation models applied to aeroacoustic and vibroacoustic problems}, series = {Advances in Aircraft and Spacecraft Science}, volume = {6}, journal = {Advances in Aircraft and Spacecraft Science}, number = {6}, publisher = {Techno Press}, issn = {2287-528X}, doi = {10.12989/aas.2019.6.6.531}, pages = {529 -- 550}, year = {2019}, language = {en} } @article{EblingKrummPfeiffelmannetal.2016, author = {Ebling, Dirk and Krumm, A. and Pfeiffelmann, Bj{\"o}rn and Gottschald, J. and Bruchmann, J. and Benim, Ali Cemal and Adam, Mario and Labs, R. and Herbertz, R. R. and Stunz, A.}, title = {Development of a System for Thermoelectric Heat Recovery from Stationary Industrial Processes}, series = {Journal of Electronic Materials}, volume = {45}, journal = {Journal of Electronic Materials}, number = {7}, publisher = {Springer}, doi = {10.1007/s11664-016-4511-8}, pages = {3433 -- 3439}, year = {2016}, language = {de} } @article{Spiegel2018, author = {Spiegel, Thomas}, title = {Impact of Renewable Energy Expansion to the Balancing Energy Demand of Differential Balancing Groups}, series = {Journal of Sustainable Development of Energy, Water and Environment Systems}, volume = {6}, journal = {Journal of Sustainable Development of Energy, Water and Environment Systems}, number = {4}, publisher = {SDEWES}, issn = {1848-9257}, doi = {10.13044/j.sdewes.d6.0215}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-29207}, pages = {784 -- 799}, year = {2018}, abstract = {The research addresses the present situation on the German electricity market caused by variable renewable energy sources. The increasing number of households with photovoltaic and battery storage systems and their impacts require special attention. These systems change the traditional electricity customer from the sole electricity consumer to an electricity producer and consumer in one person. These so-called prosumers differ in their actual electricity demand from the initially estimated electricity demand with standard load profile. This discrepancy results in deviations within a differential balancing group. Thereby, the main finding of this research is a significant higher balancing energy demand with the expansion of photovoltaic and battery storage systems. Hence, the standard load profile is particularly not suitable for differential balancing groups with a high penetration of solar systems and still less suitable for groups with solar and battery storage systems}, language = {en} } @article{FrankRoedderNeefetal.2021, author = {Frank, Lena and R{\"o}dder, Maximilian and Neef, Matthias and Adam, Mario}, title = {Heating, Ventilation, Domestic Appliances - an Energy Integrated System Concept for the Household of the Future}, series = {Energy}, volume = {234}, journal = {Energy}, publisher = {Elsevier}, issn = {0360-5442}, doi = {10.1016/j.energy.2021.121303}, year = {2021}, language = {en} } @article{RoedderFrankKirschneretal.2018, author = {R{\"o}dder, Maximilian and Frank, Lena and Kirschner, Daniel and Neef, Matthias and Adam, Mario}, title = {EnergiBUS4home - Sustainable energy resourcing in low-energy buildings}, series = {Energy}, volume = {159}, journal = {Energy}, publisher = {Elsevier}, doi = {10.1016/j.energy.2018.06.145}, pages = {638 -- 647}, year = {2018}, language = {en} } @article{ReichGottschaldRiegebaueretal.2020, author = {Reich, Marius and Gottschald, Jonas and Riegebauer, Philipp and Adam, Mario}, title = {Predictive Control of District Heating System Using Multi-Stage Nonlinear Approximation with Selective Memory}, series = {Energies}, volume = {13}, journal = {Energies}, number = {24}, publisher = {MDPI}, issn = {1996-1073}, doi = {10.3390/en13246714}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-33627}, year = {2020}, language = {en} } @article{MuenchPriesmannReichetal.2024, author = {M{\"u}nch, Justin and Priesmann, Jan and Reich, Marius and Tillmanns, Marius and Praktiknjo, Aaron and Adam, Mario}, title = {Uplifting the Complexity of Analysis for Probabilistic Security of Electricity Supply Assessments using Artificial Neural Networks}, series = {Energy and AI}, journal = {Energy and AI}, publisher = {Elsevier}, issn = {2666-5468}, doi = {10.1016/j.egyai.2024.100401}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-45359}, year = {2024}, abstract = {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.}, language = {en} } @article{PriesmannMuenchTillmannsetal.2024, author = {Priesmann, Jan and M{\"u}nch, Justin and Tillmanns, Marius and Ridha, E. and Spiegel, Thomas and Reich, Marius and Adam, Mario and Nolting, L. and Praktiknjo, Aaron}, title = {Artificial intelligence and design of experiments for resource adequacy assessment in power systems}, series = {Energy Strategy Reviews}, volume = {53}, journal = {Energy Strategy Reviews}, publisher = {Elsevier}, issn = {2211-467X}, doi = {10.1016/j.esr.2024.101368}, year = {2024}, language = {en} }