The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts

Kath, Christopher; Ziel, Florian

doi:10.1016/j.eneco.2018.10.005

Cited by 64 publications

(39 citation statements)

References 42 publications

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“…where ε i are in-sample residuals of the model and D is the size of the calibration window. In this study we compare model performance for both back-transformations to assess the loss in predictive power across models of different complexity when using the more popular [4,9,11,17,18], simpler and faster to compute, but generally incorrect transformation (3), instead of (4).…”

Section: Variance Stabilizing Transformationmentioning

confidence: 99%

“…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]. In this continuous-time market, the majority of trading takes place in the last couple of hours before gate closure [13] and on the hourly products [1]; the latter are traded from 15:00 on day d − 1 until 5 min before the delivery starts on day d, or 30 min before if the trade is made between the delivery zones.…”

Section: Introductionmentioning

confidence: 99%

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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: Variance Stabilizing Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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“…Moreover, the proposed methodology can be easily extended to other areas of energy forecasting (e.g., load, solar, wind) or other types of markets (e.g., intraday). Finally, the economic benefits from using more accurate probabilistic electricity price predictions could be evaluated, as is becoming more common in the point forecasting literature [35,36].…”

Section: Discussionmentioning

confidence: 99%

Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting

2019

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The recent developments in combining point forecasts of day-ahead electricity prices across calibration windows have provided an extremely simple, yet a very efficient tool for improving predictive accuracy. Here, we consider two novel extensions of this concept to probabilistic forecasting: one based on Quantile Regression Averaging (QRA) applied to a set of point forecasts obtained for different calibration windows, the other on a technique dubbed Quantile Regression Machine (QRM), which first averages these point predictions, then applies quantile regression to the combined forecast. Once computed, we combine the probabilistic forecasts across calibration windows by averaging probabilities of the corresponding predictive distributions. Our results show that QRM is not only computationally more efficient, but also yields significantly more accurate distributional predictions, as measured by the aggregate pinball score and the test of conditional predictive ability. Moreover, combining probabilistic forecasts brings further significant accuracy gains.

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“…In the energy forecasting literature, the lasso (least selection and shrinkage operator) seems to be a popular choice for shrinkage and feature selection methods in linear models; see, e.g., [15][16][17][18]. An extension of the lasso is given by the elastic net, which also has been applied [19][20][21][22][23][24][25].…”

Section: Estimation Of Proposed Nowcasting Modelmentioning

confidence: 99%

Load Nowcasting: Predicting Actuals with Limited Data

Ziel

2020

Energies

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We introduce the problem of load nowcasting to the energy forecasting literature. The recent load of the objective area is predicted based on limited available metering data within this area. Thus, slightly different from load forecasting, we are predicting the recent past using limited available metering data from the supply side of the system. Next, to an industry benchmark model, we introduce multiple high-dimensional models for providing more accurate predictions. They evaluate metered interconnector and generation unit data of different types like wind and solar power, storages, and nuclear and fossil power plants. Additionally, we augment the model by seasonal and autoregressive components to improve the nowcasting performance. We consider multiple estimation techniques based on the lassoand ridge and study the impact of the choice of the training/calibration period. The methodology is applied to a European TSO dataset from 2014 to 2019. The overall results show that in comparison to the industry benchmark, an accuracy improvement in terms of MAE and RMSE of about 60% is achieved. The best model is based on the ridge estimator and uses a specific non-standard shrinkage target. Due to the linear model structure, we can easily interpret the model output.

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The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts

Cited by 64 publications

References 42 publications

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

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

Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting

Load Nowcasting: Predicting Actuals with Limited Data

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