Oliver Grothe, Fabian Kächele, Fabian Krüger
arXiv 21 Apr 2022 · Econometrics · publishedEnergy Economics (2023) · 37 citations (OpenAlex)
arXiv:2204.10154 · PDF · DOI · OpenAlex · Extracted main text
Modeling price risks is crucial for economic decision making in energy markets. Besides the risk of a single price, the dependence structure of multiple prices is often relevant. We therefore propose a generic and easy-to-implement method for creating multivariate probabilistic forecasts based on univariate point forecasts of day-ahead electricity prices. While each univariate point forecast refers to one of the day's 24 hours, the multivariate forecast distribution models dependencies across hours. The proposed method is based on simple copula techniques and an optional time series component. We illustrate the method for five benchmark data sets recently provided by Lago et al. (2020). Furthermore, we demonstrate an example for constructing realistic prediction intervals for the weighted sum of consecutive electricity prices, as, e.g., needed for pricing individual load profiles.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lago, J., Marcjasz, G., Schutter, B.D., Weron, R (2020) Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark | 1.000 | 12 | 5 | 100% |
| 2 | Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., Wilby, R (2004) The schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields | 0.811 | 4 | 2 | 100% |
| 3 | Schefzik, R., Thorarinsdottir, T.L., Gneiting, T (2013) Uncertainty quantification in complex simulation models using ensemble copula coupling | 0.811 | 4 | 2 | 100% |
| 4 | Bundesverband der Energie- und Wasserwirtschaft e.V (2021) Standardlastprofile strom | 0.644 | 2 | 2 | 100% |
| 5 | Maciejowska, K., Nitka, W., Weron, T (2021) Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices | 0.644 | 2 | 2 | 100% |
| 6 | Diebold, F.X., Mariano, R.S (1995) Comparing predictive accuracy | 0.511 | 2 | 2 | 50% |
| 7 | Thorarinsdottir, T.L., Scheuerer, M., Heinz, C (2016) Assessing the calibration of high-dimensional ensemble forecasts using rank histograms | 0.511 | 2 | 2 | 50% |
| 8 | Chai, S., Xu, Z., Jia, Y (2019) Conditional density forecast of electricity price based on ensemble elm and logistic emos | 0.405 | 1 | 1 | 100% |
| 9 | Toubeau, J.F., Bottieau, J., Vallée, F., De Grève, Z (2019) Deep learning-based multivariate probabilistic forecasting for short-term scheduling in power markets | 0.405 | 1 | 1 | 100% |
| 10 | Hubicka, K., Marcjasz, G., Weron, R (2019) A note on averaging day-ahead electricity price forecasts across calibration windows | 0.405 | 1 | 1 | 100% |
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