Review of automated time series forecasting pipelines
Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …
energy systems and economics. Creating a forecasting model for a specific use case …
Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks
Generated synthetic time series aim to be both realistic by mirroring the characteristics of
real-world time series and useful by including characteristics that are useful for subsequent …
real-world time series and useful by including characteristics that are useful for subsequent …
Net load forecasting using different aggregation levels
In the electricity grid, constantly balancing the supply and demand is critical for the network's
stability and any expected deviations require balancing efforts. This balancing becomes …
stability and any expected deviations require balancing efforts. This balancing becomes …
Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network
In various applications, probabilistic forecasts are required to quantify the inherent
uncertainty associated with the forecast. However, many existing forecasting methods still …
uncertainty associated with the forecast. However, many existing forecasting methods still …
Modeling and generating synthetic anomalies for energy and power time series
With the development of the smart grid, the number of recorded energy and power times
series increases noticeably. This increase allows for the automation of smart grid …
series increases noticeably. This increase allows for the automation of smart grid …
Using weather data in energy time series forecasting: the benefit of input data transformations
Renewable energy systems depend on the weather, and weather information, thus, plays a
crucial role in forecasting time series within such renewable energy systems. However …
crucial role in forecasting time series within such renewable energy systems. However …
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
Forecasting the power generation of locally distributed PhotoVoltaic plants is vital for the
efficient operation of Smart Grids. The automated design of such models for PV plants …
efficient operation of Smart Grids. The automated design of such models for PV plants …
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information
Probabilistic forecasts are essential for various downstream applications such as business
development, traffic planning, and electrical grid balancing. Many of these probabilistic …
development, traffic planning, and electrical grid balancing. Many of these probabilistic …
[HTML][HTML] ForeTiS: A comprehensive time series forecasting framework in Python
Time series forecasting is a research area with applications in various domains,
nevertheless without yielding a predominant method so far. We present ForeTiS, a …
nevertheless without yielding a predominant method so far. We present ForeTiS, a …
[PDF][PDF] Evaluation of transformer architectures for electrical load time-series forecasting
Accurate forecasts of the electrical load are needed to stabilize the electrical grid and
maximize the use of renewable energies. Many good forecasting methods exist, including …
maximize the use of renewable energies. Many good forecasting methods exist, including …