5 papers
Probabilistic Multi-Regional Solar Power Forecasting with Any-Quantile Recurrent Neural Networks
Slawek Smyl, PaweÅ PeÅka, Grzegorz Dudek
The increasing penetration of photovoltaic (PV) generation introduces significant uncertainty into power system operation, necessitating forecasting approaches that extend beyond d…
Multivariate Forecasting of Bitcoin Volatility with Gradient Boosting: Deterministic, Probabilistic, and Feature Importance Perspectives
Grzegorz Dudek, Mateusz Kasprzyk, PaweÅ PeÅka
This study investigates the application of the Light Gradient Boosting Machine (LGBM) model for both deterministic and probabilistic forecasting of Bitcoin realized volatility. Uti…
Forecasting Cryptocurrency Prices using Contextual ES-adRNN with Exogenous Variables
Slawek Smyl, Grzegorz Dudek, PaweÅ PeÅka
In this paper, we introduce a new approach to multivariate forecasting cryptocurrency prices using a hybrid contextual model combining exponential smoothing (ES) and recurrent neur…
Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting
Mateusz Kasprzyk, PaweÅ PeÅka, Boris N. Oreshkin +1
This paper presents an enhanced N-BEATS model, N-BEATS*, for improved mid-term electricity load forecasting (MTLF). Building on the strengths of the original N-BEATS architecture,…
Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand
Slawek Smyl, Boris N. Oreshkin, PaweÅ PeÅka +1
Power systems operate under uncertainty originating from multiple factors that are impossible to account for deterministically. Distributional forecasting is used to control and mi…