Christopher Kath, Florian Ziel
arXiv 20 May 2019 · Econometrics · publishedInternational Journal of Forecasting (2020) · 56 citations (OpenAlex)
arXiv:1905.07886 · PDF · DOI · OpenAlex · Extracted main text
We discuss a concept denoted as Conformal Prediction (CP) in this paper. While initially stemming from the world of machine learning, it was never applied or analyzed in the context of short-term electricity price forecasting. Therefore, we elaborate the aspects that render Conformal Prediction worthwhile to know and explain why its simple yet very efficient idea has worked in other fields of application and why its characteristics are promising for short-term power applications as well. We compare its performance with different state-of-the-art electricity price forecasting models such as quantile regression averaging (QRA) in an empirical out-of-sample study for three short-term electricity time series. We combine Conformal Prediction with various underlying point forecast models to demonstrate its versatility and behavior under changing conditions. Our findings suggest that Conformal Prediction yields sharp and reliable prediction intervals in short-term power markets. We further inspect the effect each of Conformal Prediction's model components has and provide a path-based guideline on how to find the best CP model for each market.
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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 | Nowotarski, J., & Weron, R (2014) Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of nord pool spot prices | 1.000 | 8 | 5 | 100% |
| 2 | Nowotarski, J., & Weron, R (2018) Recent advances in electricity price forecasting: A review of probabilistic forecasting | 0.965 | 10 | 6 | 90% |
| 3 | Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndma… (2016) Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond | 0.843 | 3 | 3 | 100% |
| 4 | Nowotarski, J., & Weron, R (2015) Computing electricity spot price prediction intervals using quantile regression and forecast averaging | 0.737 | 3 | 2 | 100% |
| 5 | Yeo, I.-K., & Johnson, R. A (2000) A new family of power transformations to improve normality or symmetry | 0.644 | 4 | 1 | 100% |
| 6 | Amjady, N., & Hemmati, M (2006) Energy price forecasting-problems and proposals for such predictions | 0.644 | 2 | 2 | 100% |
| 7 | Maciejowska, K., & Nowotarski, J (2016) A hybrid model for gefcom2014 probabilistic electricity price forecasting | 0.644 | 2 | 2 | 100% |
| 8 | Maciejowska, K., Nowotarski, J., & Weron, R (2016) Probabilistic forecasting of electricity spot prices using factor quantile regression averaging | 0.644 | 2 | 2 | 100% |
| 9 | Shafer, G., & Vovk, V (2008) A tutorial on conformal prediction | 0.585 | 3 | 1 | 100% |
| 10 | Johansson, U., Boström, H., Löfström, T., & Linusson, H (2014) Regression conformal prediction with random forests | 0.511 | 2 | 1 | 100% |
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