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Adaptively coping with concept drifts in energy time series forecasting using profiles., , , , and . e-Energy, page 459-470. ACM, (2022)Forecasting energy time series with profile neural networks., , , , and . e-Energy, page 220-230. ACM, (2020)KIU-2021 Keynote: Uncertainty in Machine Learning for Environmental Research.. GI-Jahrestagung, volume P-314 of LNI, page 227. Gesellschaft für Informatik, Bonn, (2021)Data-Driven Methods for Demand-Side Flexibility in Energy Systems. Karlsruhe University, Germany, (2020)base-search.net (ftubkarlsruhe:oai:EVASTAR-Karlsruhe.de:1000123985).A comprehensive modelling framework for demand side flexibility in smart grids., , , and . Comput. Sci. Res. Dev., 33 (1-2): 13-23 (2018)Inductive biases in deep learning models for weather prediction, , , , , , , , , and 1 other author(s). (2024)Time Series Analysis for Big Data: Evaluating Bayesian Structural Time Series using Electricity Prices., , and . MKWI, page 1569-1580. Universitätsverlag Ilmenau, (2016)pyWATTS: Python Workflow Automation Tool for Time Series., , , , , , , , , and . CoRR, (2021)Industrial Demand-Side Flexibility: A Benchmark Data Set., , , and . e-Energy, page 460-473. ACM, (2019)miRNATissueAtlas2: an update to the human miRNA tissue atlas., , , , , , , , , and 6 other author(s). Nucleic Acids Res., 50 (D1): 211-221 (2022)