@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} } @inproceedings{NoltingSpiegelReichetal.2018, 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 (Best Application Paper Award)}, series = {CIE - 48th International Conference on Computers \& Industrial Engineering, 02.-05.12.2018, Auckland / New Zealand}, booktitle = {CIE - 48th International Conference on Computers \& Industrial Engineering, 02.-05.12.2018, Auckland / New Zealand}, address = {Auckland}, year = {2018}, 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} } @inproceedings{DoerrMuenchAdam2024, author = {D{\"o}rr, Dominik and M{\"u}nch, Justin and Adam, Mario}, title = {Hochaufgel{\"o}ste Langzeitprognose des deutschen Strom-Importpotenzials mithilfe von Methoden des Maschinellen Lernens}, series = {RET.Con 2024. 7. Regenerative Energietechnik Konferenz in Nordhausen, 08.-09. Februar 2024. Tagungsband}, booktitle = {RET.Con 2024. 7. Regenerative Energietechnik Konferenz in Nordhausen, 08.-09. Februar 2024. Tagungsband}, editor = {Wesselak, Viktor}, address = {Nordhausen}, organization = {Hochschule Nordhausen}, isbn = {978-3-940820-23-5}, pages = {71 -- 78}, year = {2024}, language = {de} }