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

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 naive 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 naive forecast.

[1]  Florian Ziel,et al.  Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure , 2015, IEEE Transactions on Power Systems.

[2]  Florian Ziel,et al.  Estimation and simulation of the transaction arrival process in intraday electricity markets. , 2019 .

[3]  Grzegorz Marcjasz,et al.  A Note on Averaging Day-Ahead Electricity Price Forecasts Across Calibration Windows , 2019, IEEE Transactions on Sustainable Energy.

[4]  R. Weron Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .

[5]  Christopher Kath,et al.  The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts , 2018, Energy Economics.

[6]  Ricardo J. Bessa,et al.  Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model , 2017 .

[7]  Florian Ziel,et al.  Econometric modelling and forecasting of intraday electricity prices , 2018, Journal of Commodity Markets.

[8]  Halbert White,et al.  Tests of Conditional Predictive Ability , 2003 .

[9]  Christopher Kath Modeling Intraday Markets under the New Advances of the Cross-Border Intraday Project (XBID): Evidence from the German Intraday Market , 2019, Energies.

[10]  Florian Ziel,et al.  Variance Stabilizing Transformations for Electricity Spot Price Forecasting , 2018, IEEE Transactions on Power Systems.

[11]  Aitor Ciarreta,et al.  Modeling and forecasting realized volatility in German–Austrian continuous intraday electricity prices , 2017 .

[12]  Florentina Paraschiv,et al.  Econometric Analysis of 15-Minute Intraday Electricity Prices , 2016 .

[13]  Florian Steinke,et al.  Forecasting the Price Distribution of Continuous Intraday Electricity Trading , 2019, Energies.

[14]  Tomasz Weron,et al.  Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits , 2019, Energies.

[15]  Jakub Nowotarski,et al.  Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting , 2016 .

[16]  Florian Ziel,et al.  Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices , 2018, International Journal of Forecasting.

[17]  R. Weron,et al.  Recent advances in electricity price forecasting: A review of probabilistic forecasting , 2016 .

[18]  Jakub Nowotarski,et al.  An empirical comparison of alternate schemes for combining electricity spot price forecasts , 2013 .

[19]  Derek Bunn,et al.  The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices , 2019, Energy Policy.

[20]  Katarzyna Maciejowska,et al.  Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach , 2020, Energy Economics.

[21]  Ignacio J. Ramirez-Rosado,et al.  Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market , 2016 .

[22]  Francis X. Diebold,et al.  Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives , 2018, International Journal of Forecasting.

[23]  Rafał Weron,et al.  Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO , 2018, International Journal of Forecasting.

[24]  Ilkay Oksuz,et al.  Neural Network Based Model Comparison for Intraday Electricity Price Forecasting , 2019 .

[25]  Jakub Nowotarski,et al.  On the importance of the long-term seasonal component in day-ahead electricity price forecasting , 2016, Energy Economics.

[26]  Rafał Weron,et al.  Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks , 2018, 1805.06649.

[27]  Javier Reneses,et al.  Electricity price forecasting in the short term hybridising fundamental and econometric modelling , 2017, Electric Power Systems Research.

[28]  Rafał Weron,et al.  Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models , 2018, Energies.

[29]  Derek W. Bunn,et al.  A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market , 2018, Energies.

[30]  Rafał Weron,et al.  On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks , 2019, International Journal of Forecasting.