arXiv subjects

Bartosz Uniejewski

Publications and source records attributed to Bartosz Uniejewski.

12 recordsLinked to original sources

arXiv2026

The role of probabilistic load and renewable prediction in enhancing day-ahead electricity price forecasts

The increasing penetration of renewable energy sources (RES) has amplified volatility in electricity markets, while demand variability continues to challenge system stability. Traditional day-ahead electricity price forecasting (EPF) models rely on point forecasts of load and RES generation, which fail to capture the uncertainty inherent in weather-driven supply and dynamic demand. This study introduces a novel framework that integrates probabilistic forecasts of load, wind, and solar generation into EPF models. Using data from the German EPEX market between 2015 and 2023, we generate quantile forecasts of fundamental variables via historical simulation, conformal prediction and quantile regression, and incorporate them into both parsimonious expert and high-dimensional LASSO-based models. Empirical results show that probabilistic inputs substantially enhance forecast accuracy, reducing root mean square errors by up to 13% compared with point-forecast benchmarks. Notably, extreme quantiles of load and RES forecasts emerge as the most influential predictors, underscoring the importance of rare but system-critical scenarios. These findings demonstrate that accounting for uncertainty in both load and renewables is crucial for reliable electricity price forecasting and offers practical value for system operators, traders, and policymakers navigating renewable integration.

stat.AP↗

arXiv2026

Statistical and economic evaluation of forecasts in electricity markets: beyond RMSE and MAE

Electricity price forecasts are typically evaluated using accuracy measures such as RMSE and MAE, although these metrics often fail to reflect their economic value in operational decisions. This paper investigates which statistical properties of electricity price forecasts are most relevant for economic performance, using battery energy storage system (BESS) arbitrage as an application. We assess prediction quality along four dimensions: forecast accuracy, intraday error dispersion, association between predicted and realized prices, and the ability to identify daily price extrema. We construct a comprehensive pool of 192 hourly day-ahead electricity price forecasts and use it to evaluate the relationship between proposed quality measures and profits generated for two representative BESS configurations. The results show that traditional accuracy metrics are only weakly correlated with BESS income. At the same time, dispersion- and association-based measures better capture a forecast's economic value by reflecting its ability to reproduce daily price patterns. These findings demonstrate that incorporating complementary evaluation criteria may improve forecast selection and enhance the economic performance of BESS.

q-fin.CP↗

arXiv2026

Variance Stabilizing Transformations for Electricity Price Forecasting in Periods of Increased Volatility

Accurate day-ahead electricity price forecasts are critical for power system operation and market participation, yet growing renewable penetration and recent crises have caused unprecedented volatility that challenges standard models. This paper revisits variance stabilizing transformations (VSTs) as a preprocessing tool by introducing a novel parametrization of the asinh transformation, systematically analyzing parameter sensitivity and calibration window size, and explicitly testing performance under volatile market regimes. Using data from Germany, Spain, and France over 2015-2024 with two model classes (NARX and LEAR), we show that VSTs substantially reduce forecast errors, with gains of up to 14.6% for LEAR and 8.7% for NARX relative to untransformed benchmarks. The new parametrized asinh consistently outperforms its standard form, while rolling averaging across transformations delivers the most robust improvements, reducing errors by up to 17.7%. Results demonstrate that VSTs are especially valuable in volatile regimes, making them a powerful tool for enhancing electricity price forecasting in today's power markets.

stat.AP↗

arXiv2025

Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts

Quantifying the uncertainty of forecasting models is essential to assess and mitigate the risks associated with data-driven decisions, especially in volatile domains such as electricity markets. Machine learning methods can provide highly accurate electricity price forecasts, critical for informing the decisions of market participants. However, these models often lack uncertainty estimates, which limits the ability of decision makers to avoid unnecessary risks. In this paper, we propose a novel method for generating probabilistic forecasts from ensembles of point forecasts, called Isotonic Quantile Regression Averaging (iQRA). Building on the established framework of Quantile Regression Averaging (QRA), we introduce stochastic order constraints to improve forecast accuracy, reliability, and computational costs. In an extensive forecasting study of the German day-ahead electricity market, we show that iQRA consistently outperforms state-of-the-art postprocessing methods in terms of both reliability and sharpness. It produces well-calibrated prediction intervals across multiple confidence levels, providing superior reliability to all benchmark methods, particularly coverage-based conformal prediction. In addition, isotonic regularization decreases the complexity of the quantile regression problem and offers a hyperparameter-free approach to variable selection.

cs.LG↗

arXiv2025

Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market

Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the remaining part, predicting both separately, and then combining their forecasts can bring significant accuracy gains in day-ahead electricity price forecasting. However, not much attention has been paid to predicting the LTSC, and the last 24 hourly values of the estimated pattern are typically copied for the target day. To address this gap, we introduce a novel approach which extracts the trend-seasonal pattern from a price series extrapolated using price forecasts for the next 24 hours. We assess it using two 5-year long test periods from the German and Spanish power markets, covering the Covid-19 pandemic, the 2021/2022 energy crisis, and the war in Ukraine. Considering parsimonious autoregressive and LASSO-estimated models, we find that improvements in predictive accuracy range from 3\% to 15\% in terms of the root mean squared error and exceed 1\% in terms of profits from a realistic trading strategy involving day-ahead bidding and battery storage.

q-fin.ST↗

arXiv2024

Ranking probabilistic forecasting models with different loss functions

In this study, we introduced various statistical performance metrics, based on the pinball loss and the empirical coverage, for the ranking of probabilistic forecasting models. We tested the ability of the proposed metrics to determine the top performing forecasting model and investigated the use of which metric corresponds to the highest average per-trade profit in the out-of-sample period. Our findings show that for the considered trading strategy, ranking the forecasting models according to the coverage of quantile forecasts used in the trading hours exhibits a superior economic performance.

econ.EM↗

arXiv2024

Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading

This paper presents a novel hybrid approach for constricting probabilistic forecasts that combines both the Quantile Regression Averaging (QRA) method and the factor-based averaging scheme. The performance of the approach is evaluated on data sets from two European energy markets - the German EPEX SPOT and the Polish Power Exchange (TGE). The results show that the newly proposed method outperforms literature benchmarks in terms of statistical measures: the empirical coverage and the Christoffersen test for conditional coverage. Moreover, in line with recent literature trends, the economic value of forecasts is evaluated based on the trading strategy using probabilistic price predictions to optimize the operation of an energy storage system. The results suggest that apart from the use of statistical measures, there is a need for the economic evaluation of forecasts.

stat.AP↗

arXiv2024

Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices

Accurate short-term price forecasting is essential for daily operations in electricity markets. This article introduces a new method, called Smoothing Quantile Regression (SQR) Averaging, that improves upon well-performing probabilistic forecasting schemes. To demonstrate its utility, a comprehensive study is conducted on two electricity markets, including recent data covering the COVID-19 pandemic and the Russian invasion of Ukraine. The performance of SQR Averaging is evaluated both in terms of reliability and sharpness measures, and economic benefits from a trading strategy. The latter utilizes battery storage and sets limit orders using selected quantiles of the predictive distribution. SQR Averaging leads to profit increases of up to 3.5\% on average compared to the benchmark strategy based solely on point forecasts. This is strong evidence for the practical value of using probabilistic forecasts in day-ahead power trading, even in the face of the COVID-19 pandemic and geopolitical disruptions.

stat.AP↗

arXiv2024

Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression

Operational decisions relying on predictive distributions of electricity prices can result in significantly higher profits compared to those based solely on point forecasts. However, the majority of models developed in both academic and industrial settings provide only point predictions. To address this, we examine three postprocessing methods for converting point forecasts of day-ahead electricity prices into probabilistic ones: Quantile Regression Averaging, Conformal Prediction, and the recently introduced Isotonic Distributional Regression. We find that while the latter demonstrates the most varied behavior, it contributes the most to the ensemble of the three predictive distributions, as measured by Shapley values. Remarkably, the performance of the combination is superior to that of state-of-the-art Distributional Deep Neural Networks over two 4.5-year test periods from the German and Spanish power markets, spanning the COVID pandemic and the war in Ukraine.

q-fin.ST↗

arXiv2024

Regularization for electricity price forecasting

The most commonly used form of regularization typically involves defining the penalty function as a L1 or L2 norm. However, numerous alternative approaches remain untested in practical applications. In this study, we apply ten different penalty functions to predict electricity prices and evaluate their performance under two different model structures and in two distinct electricity markets. The study reveals that LQ and elastic net consistently produce more accurate forecasts compared to other regularization types. In particular, they were the only types of penalty functions that consistently produced more accurate forecasts than the most commonly used LASSO. Furthermore, the results suggest that cross-validation outperforms Bayesian information criteria for parameter optimization, and performs as well as models with ex-post parameter selection.

stat.AP↗

arXiv2022

LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling

This paper develops a novel, fully automated forecast averaging scheme, which combines LASSO estimation method with Principal Component Averaging (PCA). LASSO-PCA (LPCA) explores a pool of predictions based on a single model but calibrated to windows of different sizes. It uses information criteria to select tuning parameters and hence reduces the impact of researchers' at hock decisions. The method is applied to average predictions of hourly day-ahead electricity prices over 650 point forecasts obtained with various lengths of calibration windows. It is evaluated on four European and American markets with almost two and a half year of out-of-sample period and compared to other semi- and fully automated methods, such as simple mean, AW/WAW, LASSO and PCA. The results indicate that the LASSO averaging is very efficient in terms of forecast error reduction, whereas PCA method is robust to the selection of the specification parameter. LPCA inherits the advantages of both methods and outperforms other approaches in terms of MAE, remaining insensitive the the choice of a tuning parameter.

stat.AP↗

arXiv2022

Forecasting Electricity Prices

Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. Although it aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure. On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.

q-fin.ST↗