Dariusz Baczyński

Increasing variability in electricity production and demand contributes to greater electricity price volatility across different markets. The effective implementation of business processes related to broadly defined electricity trading therefore requires reliable electricity price forecasts. Consequently, electricity price forecasting has grown in both importance and research interest. This article presents an in-depth literature review of short-term point electricity price forecasting at the native temporal resolutions of the reviewed markets, primarily hourly and 30 min intervals, with selected studies using 15 min and 5 min intervals. The review concerns forecasts of individual market-interval prices and does not address forecasts of daily aggregated prices. The analysis covers the input data used in forecasting models, the main forecasting approaches, modelling techniques, and error measures. Forecast quality is examined with respect to market characteristics, forecasting methods, and explanatory variables. General trends in the reported results are identified, together with descriptive relationships among selected error measures. Particular attention is given to the RMSE-to-MAE ratio, referred to in this review as the Error Dispersion Factor (EDF), which is treated solely as a descriptive summary of the relative inequality of absolute forecast-error magnitudes in a given sample. The article concludes with findings and recommendations concerning best practices in electricity price forecasting. The review differs from broader conceptual and market-specific surveys by focusing narrowly on short-term point forecasts for individual market delivery intervals and by providing a structured quantitative synthesis of studies published between January 2021 and May 2026.

Electricity price forecasting has traditionally relied on point predictions, which provide a single expected value for each future delivery period. However, the high volatility, spikes, heavy tails, negative prices and regime changes observed in modern electricity markets make point forecasts insufficient for many trading, bidding, storage, scheduling and risk-management decisions. This review focuses on probabilistic electricity price forecasting (PEPF), which represents uncertainty through quantiles, prediction intervals, predictive densities and multivariate scenarios. It systematizes recent developments in post-processing methods, Quantile Regression Averaging, conformal prediction, Bayesian and heteroscedastic models, distributional neural networks, copula-based approaches, normalizing flows, generative models and scenario generation. Particular attention is paid to probabilistic forecast evaluation, including proper scoring rules, calibration diagnostics and statistical testing. The review highlights the transition from marginal uncertainty quantification toward coherent multivariate and decision-oriented forecasting, and identifies open challenges related to calibration, dependence modeling, benchmark design, economic value assessment and reproducibility.

Forecasting plays a crucial role in power systems because it directly influences the stability, efficiency, and economic operation of the electricity grid

High-quality short-term forecasts of wind farm generation are crucial for the dynamically developing renewable energy generation sector. This article addresses the selection of appropriate gradient-boosted decision tree models (GBDT) for forecasting wind farm energy generation with a 10-min time horizon. In most forecasting studies, authors utilize a single gradient-boosted decision tree model and compare its performance with other machine learning (ML) techniques and sometimes with a naive baseline model. This paper proposes a comprehensive comparison of all gradient-boosted decision tree models (GBDTs, eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)) used for forecasting. The objective is to evaluate each model in terms of forecasting accuracy for wind farm energy generation (forecasting error) and computational time during model training. Computational time is a critical factor due to the necessity of testing numerous models with varying hyperparameters to identify the optimal settings that minimize forecasting error. Forecast quality using default hyperparameters is used here as a reference. The research also seeks to determine the most effective sets of input variables for the predictive models. The article concludes with findings and recommendations regarding the preferred GBDT models. Among the four tested models, the oldest GBDT model demonstrated a significantly longer training time, which should be considered a major drawback of this implementation of gradient-boosted decision trees. In terms of model quality testing, the lowest nRMSE error was achieved by the oldest model—GBDT in its tuned version (with the best hyperparameter values obtained from exploring 40,000 combinations).

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Paper concerns the novel modelling technology called Digital Twin in aspect of physical phenomenon simulation analysis. Digital Twin model concerning electrodynamic forces in three-phase high voltage disconnector was presented, validated and discussed. Experiences related to laboratory work and measurement of electrodynamic forces clearly showed that often, despite the use of modern computing machines, the research process is hard, very expensive and values of electrodynamic forces are almost impossible to measure in real scale conditions. Hence the idea to use very advanced field models minimized to a reduced order model (ROM), using the Digital Twin Technology seemed valid and rendered possibilities for advancements concerning this type of research. Due to the large weight of devices (chokes, transformers, disconnectors) on high-voltage networks it is often time-consuming or even impossible to perform the entire simulation. Proposed modelling approach helps to obtain results from very complex models also for long-term thermal tests. The reduced order model can be used not only by researchers, but also in the development departments of enterprises. Digital Twin model results were compared and validated with values derived from short-circuit tests made in Institute of Power Engineering, Research Institute.

W artykule przedstawiono praktyczne podejście do optymalizacji konfiguracji rzeczywistej sieci rozdzielczej nn. Opisano zbiór danych wejściowych, metody optymalizacji i uzyskane wyniki. Na podstawie prognoz obciążenia w poszczególnych węzłach odbiorczych określono optymalne konfiguracje. Prognozy wykonano czterema metodami prognostycznymi. Optymalizację konfiguracji sieci przeprowadzono za pomocą dwóch opracowanych metod: heurystycznej i genetycznej. Na podstawie przeprowadzonych symulacji sformułowano praktyczne wnioski.

A modern power system is a complex network of interconnected components, such as generators, transmission lines, and distribution subsystems, that are designed to provide electricity to consumers in an efficient and reliable manner

Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation forecasts in wind farms. In addition, extensive quantitative and qualitative analyses have been conducted to assess the magnitude of forecasting error depending on selected factors (such as forecasting horizon, wind farm size, and a class of the forecasting method). Based on these analyses and a review of more than one hundred papers, a unique set of recommendations on the preferred content of papers addressing wind farm generation forecasts has been developed. These recommendations would make it possible to conduct very precise benchmarking meta-analyses of forecasting studies described in research papers and to develop valuable general conclusions concerning the analyzed phenomena.

The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using seven methods), forecast annual power demand arising solely out of the operation of the forecast number of electric vehicles, forecast annual power demand with and without the impact of e-mobility growth (using six methods), forecast daily profiles of typical days with and without the impact of e-mobility growth (using three methods). For the purpose of this research, we developed a unique Growth Dynamics Model to forecast the number of electric vehicles in Poland. The application of Multi-Layer Perceptron (MLP) to the extrapolation of non-linear functions (to the forecast number of electric vehicles and forecast annual power demand without the impact of e-mobility growth) is our original, unique proposal to use the Artificial Neural Network (ANN). Another unique, innovative proposal is to include Artificial Neural Networks (Multi-Layer Perceptron and Long short-term memory (LSTM)) in an Ensemble Model for simultaneous extrapolation of 24 non-linear functions to forecast daily profiles of typical days without taking e-mobility into account. This research determined the impact of e-mobility development on the Polish power system, both in terms of annual growth of demand for power and within particular days (hourly distribution) for two typical days (summer and winter). Under the (most likely) balanced growth variant of annual demand for power, due to e-mobility, such demand would grow by more than 4%, and almost 7% under the optimistic variant. Percentage growth of power demand in terms of variation according to time of day was determined. For instance, for the balanced variant, the largest percentage share of e-mobility was in the evening “peak” time (about 6%), and the smallest percentage was in the night “valley” (about 2%).