| Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests N Ludwig, S Feuerriegel, D Neumann Journal of Decision Systems 24 (1), 19-36, 2015 | 132 | 2015 |
| Data analytics in the electricity sector–A quantitative and qualitative literature review F vom Scheidt, H Medinová, N Ludwig, B Richter, P Staudt, C Weinhardt Energy and AI 1, 100009, 2020 | 79 | 2020 |
| A comprehensive modelling framework for demand side flexibility in smart grids L Barth, N Ludwig, E Mengelkamp, P Staudt Computer Science-Research and Development 33 (1), 13-23, 2018 | 51 | 2018 |
| pyWATTS: Python workflow automation tool for time series B Heidrich, A Bartschat, M Turowski, O Neumann, K Phipps, ... arXiv preprint arXiv:2106.10157, 2021 | 23 | 2021 |
| Mining flexibility patterns in energy time series from industrial processes N Ludwig, S Waczowicz, R Mikut, V Hagenmeyer, F Hoffmann, ... Proceedings. 27. Workshop Computational Intelligence Dortmund, 23. - 24 …, 2017 | 22 | 2017 |
| Concept and benchmark results for Big Data energy forecasting based on Apache Spark JÁ González Ordiano, A Bartschat, N Ludwig, E Braun, S Waczowicz, ... Journal of Big Data 5, 1-11, 2018 | 19 | 2018 |
| How much demand side flexibility do we need? Analyzing where to exploit flexibility in industrial processes L Barth, V Hagenmeyer, N Ludwig, D Wagner Proceedings of the Ninth International Conference on Future Energy Systems …, 2018 | 19 | 2018 |
| Forecasting energy time series with profile neural networks B Heidrich, M Turowski, N Ludwig, R Mikut, V Hagenmeyer Proceedings of the eleventh acm international conference on future energy …, 2020 | 18 | 2020 |
| Evaluating ensemble post‐processing for wind power forecasts K Phipps, S Lerch, M Andersson, R Mikut, V Hagenmeyer, N Ludwig Wind Energy 25 (8), 1379-1405, 2022 | 16 | 2022 |
| Industrial demand-side flexibility: A benchmark data set N Ludwig, L Barth, D Wagner, V Hagenmeyer Proceedings of the Tenth ACM International Conference on Future Energy …, 2019 | 16 | 2019 |
| A collection and categorization of open‐source wind and wind power datasets N Effenberger, N Ludwig Wind Energy 25 (10), 1659-1683, 2022 | 14 | 2022 |
| Towards coding strategies for forecasting-based scheduling in smart grids and the energy lab 2.0 W Jakob, JÁG Ordiano, N Ludwig, R Mikut, V Hagenmeyer Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2017 | 13 | 2017 |
| Sizing of hybrid energy storage systems using recurring daily patterns S Karrari, N Ludwig, G De Carne, M Noe IEEE Transactions on Smart Grid 13 (4), 3290-3300, 2022 | 12 | 2022 |
| Net load forecasting using different aggregation levels M Beichter, K Phipps, MM Frysztacki, R Mikut, V Hagenmeyer, N Ludwig Energy Informatics 5 (Suppl 1), 19, 2022 | 11 | 2022 |
| Sciber: A new public data set of municipal building consumption P Staudt, N Ludwig, J Huber, V Hagenmeyer, C Weinhardt Proceedings of the Ninth International Conference on Future Energy Systems …, 2018 | 10 | 2018 |
| Analytical uncertainty propagation for multi-period stochastic optimal power flow R Bauer, T Mühlpfordt, N Ludwig, V Hagenmeyer Sustainable Energy, Grids and Networks 33, 100969, 2023 | 8 | 2023 |
| A method for sizing centralised energy storage systems using standard patterns S Karrari, N Ludwig, V Hagenmeyer, M Noe 2019 IEEE Milan PowerTech, 1-6, 2019 | 7 | 2019 |
| Probabilistic load forecasting using post-processed weather ensemble predictions N Ludwig, S Arora, JW Taylor Journal of the Operational Research Society, 2023 | 6 | 2023 |
| Multi-horizon wind power forecasting using multi-modal spatio-temporal neural networks ES Miele, N Ludwig, A Corsini Energies 16 (8), 3522, 2023 | 4 | 2023 |
| Potential of ensemble copula coupling for wind power forecasting K Phipps, N Ludwig, V Hagenmeyer, R Mikut Proceedings 30. Workshop Computational Intelligence 26, 87, 2020 | 4 | 2020 |
Veit HagenmeyerKIT在 kit.edu 的电子邮件经过验证
