Forecasting energy time series with profile neural networks. - Benedikt Heidrich; Marian Turowski; Nicole Ludwig 002; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2020)

pyWATTS: Python Workflow Automation Tool for Time Series. - Benedikt Heidrich; Andreas Bartschat; Marian Turowski; Oliver Neumann; Kaleb Phipps; Stefan Meisenbacher; Kai Schmieder; Nicole Ludwig 002; Ralf Mikut; Veit Hagenmeyer (CoRR, 2021)

Smart Data Representations: Impact on the Accuracy of Deep Neural Networks. - Oliver Neumann; Nicole Ludwig 002; Marian Turowski; Benedikt Heidrich; Veit Hagenmeyer; Ralf Mikut (CoRR, 2021)

Adaptively coping with concept drifts in energy time series forecasting using profiles. - Benedikt Heidrich; Nicole Ludwig 002; Marian Turowski; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2022)

Enhancing anomaly detection methods for energy time series using latent space data representations. - Marian Turowski; Benedikt Heidrich; Kaleb Phipps; Kai Schmieder; Oliver Neumann; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2022)

Modeling and generating synthetic anomalies for energy and power time series. - Marian Turowski; Moritz Weber 001; Oliver Neumann; Benedikt Heidrich; Kaleb Phipps; Hüseyin Kemâl Çakmak; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2022)

Towards line-restricted dispatchable feeders using probabilistic forecasts for PV-dominated low-voltage distribution grids. - Dorina Werling; Benedikt Heidrich; Hüseyin Kemâl Çakmak; Veit Hagenmeyer (e-Energy, 2022)

Boost short-term load forecasts with synthetic data from transferred latent space information. - Benedikt Heidrich; Lisa Mannsperger; Marian Turowski; Kaleb Phipps; Benjamin Schäfer 001; Ralf Mikut; Veit Hagenmeyer (Energy Inform., 2022)

AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models. - Stefan Meisenbacher; Benedikt Heidrich; Tim Martin; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2023)

Loss-Customised Probabilistic Energy Time Series Forecasts Using Automated Hyperparameter Optimisation. - Kaleb Phipps; Stefan Meisenbacher; Benedikt Heidrich; Marian Turowski; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2023)

The Impact of Forecast Characteristics on the Forecast Value for the Dispatchable Feeder. - Dorina Werling; Maximilian Beichter; Benedikt Heidrich; Kaleb Phipps; Ralf Mikut; Veit Hagenmeyer (e-Energy (Companion), 2023)

Automating Value-Oriented Forecast Model Selection by Meta-learning: Application on a Dispatchable Feeder. - Dorina Werling; Maximilian Beichter; Benedikt Heidrich; Kaleb Phipps; Ralf Mikut; Veit Hagenmeyer (EI.A, 2023)

Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks. - Kaleb Phipps; Benedikt Heidrich; Marian Turowski; Moritz Wittig; Ralf Mikut; Veit Hagenmeyer (CoRR, 2023)

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information. - Benedikt Heidrich; Kaleb Phipps; Oliver Neumann; Marian Turowski; Ralf Mikut; Veit Hagenmeyer (CoRR, 2023)

Transformer training strategies for forecasting multiple load time series. - Matthias Hertel; Maximilian Beichter; Benedikt Heidrich; Oliver Neumann; Benjamin Schäfer 001; Ralf Mikut; Veit Hagenmeyer (Energy Inform., 2023)

Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks. - Benedikt Heidrich; Marian Turowski; Kaleb Phipps; Kai Schmieder; Wolfgang Süß; Ralf Mikut; Veit Hagenmeyer (Appl. Intell., 2023)

Decision-Focused Retraining of Forecast Models for Optimization Problems in Smart Energy Systems. - Maximilian Beichter; Dorina Werling; Benedikt Heidrich; Kaleb Phipps; Oliver Neumann; Nils Friederich; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2024)

Intrinsic Explainable Artificial Intelligence Using Trainable Spatial Weights on Numerical Weather Predictions. - Oliver Neumann; Maximilian Beichter; Benedikt Heidrich; Nils Friederich; Veit Hagenmeyer; Ralf Mikut (e-Energy, 2024)

Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators. - Manuel Treutlein; Marc Schmidt; Roman Hahn; Matthias Hertel; Benedikt Heidrich; Ralf Mikut; Veit Hagenmeyer (CoRR, 2024)

Challenges in Deep Learning Based Forecasting of Time Series with Calendar-driven Periodicities - Benedikt Heidrich (2024)

Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network. - Kaleb Phipps; Benedikt Heidrich; Marian Turowski; Moritz Wittig; Ralf Mikut; Veit Hagenmeyer (Appl. Intell., 2024)

Optimizing Small Language Models for In-Vehicle Function-Calling. - Yahya Sowti Khiabani; Farris Atif; Chieh Hsu; Sven Stahlmann; Tobias Michels; Sebastian Kramer; Benedikt Heidrich; M. Saquib Sarfraz; Julian Merten; Faezeh Tafazzoli (CoRR, 2025)

Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimization. - Maximilian Beichter; Nils Friederich; Janik Pinter; Dorina Werling; Kaleb Phipps; Sebastian Beichter; Oliver Neumann; Ralf Mikut; Veit Hagenmeyer; Benedikt Heidrich (CoRR, 2025)