7 citations · 19 across the 13 of their papers we have counts for
15 papers
Probabilistic Multi-Regional Solar Power Forecasting with Any-Quantile Recurrent Neural Networks
Slawek Smyl, Paweł Pełka, 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, Paweł Pełka
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, Paweł Pełka
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, Paweł Pełka, 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, Paweł Pełka +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…
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
Slawek Smyl, Grzegorz Dudek, Paweł Pełka
In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) an…
Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study
Grzegorz Dudek, Slawek Smyl, Paweł Pełka
This paper compares recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality. The cells we compare include classic…
ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
Slawek Smyl, Grzegorz Dudek, Paweł Pełka
Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance. This paper proposes a…
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting
Slawek Smyl, Grzegorz Dudek, Paweł Pełka
Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend. This paper proposes a novel hybrid hi…
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting
Grzegorz Dudek, Paweł Pełka
In this work, we propose an ensemble forecasting approach based on randomized neural networks. Improved randomized learning streamlines the fitting abilities of individual learners…
N-BEATS neural network for mid-term electricity load forecasting
Boris N. Oreshkin, Grzegorz Dudek, Paweł Pełka +1
This paper addresses the mid-term electricity load forecasting problem. Solving this problem is necessary for power system operation and planning as well as for negotiating forward…
Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting
Paweł Pełka, Grzegorz Dudek
This work presents a Long Short-Term Memory (LSTM) network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of…
A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting
Grzegorz Dudek, Paweł Pełka, Slawek Smyl
This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting. The model combines exponential smoothing (ETS), advanced Long Short-Term Memory (LST…
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods
Paweł Pełka, Grzegorz Dudek
This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs). PSFMs applied in this study include -nearest ne…
Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study
Grzegorz Dudek, Paweł Pełka
Pattern similarity-based methods are widely used in classification and regression problems. Repeated, similar-shaped cycles observed in seasonal time series encourage to apply thes…