Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Maciejowska, Katarzyna

doi:10.1016/j.eneco.2019.104532

Cited by 108 publications

(56 citation statements)

References 26 publications

(42 reference statements)

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“…However, the rapid expansion and integration of renewable energy sources (most notably wind and solar), active demand side management (smart meters, smart appliances, etc.) and the introduction of the XBID pan-European trading platform have shifted the focus to intraday markets [3][4][5]. One of the more liquid-and hence more studied-marketplaces, is the German intraday market for quarter-hourly and hourly products [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts

2020

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In the last three decades the vast majority of electricity price forecasting (EPF) research has concerned day-ahead markets. However, the rapid expansion of renewable generation—mostly wind and solar—have shifted the focus to intraday markets, which can be used to balance the deviations between positions taken in the day-ahead market and the actual demand and renewable generation. A recent EPF study claims that the German intraday, continuous-time market for hourly products is weak-form efficient, that is, that the best predictor for the so-called ID3-Price index is the most recent transaction price. Here, we undermine this claim and show that we can beat the naïve forecast by combining it with a prediction of a parameter-rich model estimated using the least absolute shrinkage and selection operator (LASSO). We further argue, that that if augmented with timely predictions of fundamental variables for the coming hours, the LASSO-estimated model itself can significantly outperform the naïve forecast.

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Section: Introductionmentioning

confidence: 99%

Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts

2020

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“…By comparing against nearly 30 benchmarks, we provide evidence for the superior predictive performance of LQRA in terms of the Kupiec test for (unconditional) coverage, the pinball score and the test for conditional predictive accuracy (CPA). The problem of the optimal choice of λ, possibly changing across time (as in and quantiles, a more thorough prediction error analysis (e.g., performance in the center vs. in the tails of the distribution, as in Janke and Steinke, 2019) or assessing the impact of different fundamental variables on the obtained price forecasts (see, e.g., Maciejowska, 2020) are left for future research.…”

Section: Resultsmentioning

confidence: 99%

Regularized quantile regression averaging for probabilistic electricity price forecasting

Uniejewski

Weron

2021

Energy Economics

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Quantile Regression Averaging (QRA) has sparked interest in the electricity price forecasting community after its unprecedented success in the Global Energy Forecasting Competition 2014, where the top two winning teams in the price track used variants of QRA. However, recent studies have reported the method's vulnerability to low quality predictors when the set of regressors is larger than just a few. To address this issue, we consider a regularized variant of QRA, which utilizes the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically select the relevant regressors. We evaluate the introduced technique-dubbed LASSO QRA or LQRA for short-using datasets from the Polish and Nordic power markets, a set of 25 point forecasts obtained for calibration windows of different lengths and 20 different values of the regularization parameter. By comparing against nearly 30 benchmarks, we provide evidence for its superior predictive performance in terms of the Kupiec test, the pinball score and the test for conditional predictive accuracy.

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“…The development of high accuracy forecasting models is hard, due to the data presenting high frequency, volatility, non-linearity, and seasonality [4]. Moreover, climatic variables, energy demand, power supply capacity, and the impact of renewable energy sources [5][6][7] make the forecasting process a challenging task. The incorporation of exogenous variables in the forecasting models could help the models to understand the data dynamics and allow for them to obtain more accurate results.…”

Section: Introductionmentioning

confidence: 99%

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

Cited by 108 publications

References 26 publications

Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts

Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts

Regularized quantile regression averaging for probabilistic electricity price forecasting

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

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