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06/19/2023
Transformer Training Strategies for Forecasting Multiple Load Time Series
Recent work uses Transformers for load forecasting, which are the state ...
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02/06/2023
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information
Probabilistic forecasts are essential for various downstream application...
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02/03/2023
Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks
In various applications, probabilistic forecasts are required to quantif...
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01/11/2023
Non-linear, bivariate stochastic modelling of power-grid frequency applied to islands
Mitigating climate change requires a transition away from fossil fuels t...
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12/13/2022
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
Accurate PhotoVoltaic (PV) power generation forecasting is vital for the...
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11/08/2022
Distribution Grid Monitoring Based on Widely Available Smart Plugs
During the last few years, smart home devices have become increasingly p...
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09/27/2022
Predicting the power grid frequency of European islands
Modelling, forecasting and overall understanding of the dynamics of the ...
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09/06/2022
Inverse methods: How feasible are spatially low-resolved capacity expansion modeling results when dis-aggregated at high resolution?
Spatially highly-resolved capacity expansion models are computationally ...
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07/07/2022
Microscopic fluctuations in power-grid frequency recordings at the sub-second scale
Complex systems, such as the power grid, are essential for our daily liv...
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03/13/2022
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles
Data-driven building energy prediction is an integral part of the proces...
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02/28/2022
Automated generation of large-scale distribution grid models based on open data and open source software using an optimization approach
The increasing share of renewable energy sources on distribution grid le...
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02/03/2022
Review of automated time series forecasting pipelines
Time series forecasting is fundamental for various use cases in differen...
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11/17/2021
Smart Data Representations: Impact on the Accuracy of Deep Neural Networks
Deep Neural Networks are able to solve many complex tasks with less engi...
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10/26/2021
Concepts for Automated Machine Learning in Smart Grid Applications
Undoubtedly, the increase of available data and competitive machine lear...
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06/18/2021
pyWATTS: Python Workflow Automation Tool for Time Series
Time series data are fundamental for a variety of applications, ranging ...
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01/22/2021
The strong effect of network resolution on electricity system models with high shares of wind and solar
Energy system modellers typically choose a low spatial resolution for th...
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01/05/2021
Data-Driven Copy-Paste Imputation for Energy Time Series
A cornerstone of the worldwide transition to smart grids are smart meter...
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09/29/2020
Evaluating Ensemble Post-Processing for Wind Power Forecasts
Capturing the uncertainty in probabilistic wind power forecasts is chall...
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04/08/2020
PolyChaos.jl – A Julia Package for Polynomial Chaos in Systems and Control
Polynomial chaos expansion (PCE) is an increasingly popular technique fo...
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02/18/2020
A Scalable Method for Scheduling Distributed Energy Resources using Parallelized Population-based Metaheuristics
Recent years have seen an increasing integration of distributed renewabl...
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11/26/2019
A Comparison of Partitioning Strategies in AC Optimal Power Flow
The highly non-convex AC optimal power flow problem is known to scale ve...
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10/14/2019
Data-driven charging strategies for grid-beneficial, customer-oriented and battery-preserving electric mobility
Electric Vehicle (EV) penetration and renewable energies enables synergi...
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03/18/2019