Combining Predictive Distributions of Electricity Prices. Does Minimizing the CRPS Lead to Optimal Decisions in Day-Ahead Bidding?
Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no mo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wrocław University of Science and Technology
2023-01-01
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| Series: | Operations Research and Decisions |
| Online Access: | https://ord.pwr.edu.pl/assets/papers_archive/ord2023vol33no3_7.pdf |
| _version_ | 1797659284350173184 |
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| author | Weronika Nitka Rafał Weron |
| author_facet | Weronika Nitka Rafał Weron |
| author_sort | Weronika Nitka |
| collection | DOAJ |
| description | Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article, we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we conduct an empirical study using hourly day-ahead electricity prices from the German EPEX market. We find that increasing the diversity of an ensemble can have a positive impact on accuracy. At the same time, the higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits, despite significantly more accurate predictions. (original abstract) |
| first_indexed | 2024-03-11T18:12:47Z |
| format | Article |
| id | doaj.art-fb260be4cd67448da272b036ac4a44dc |
| institution | Directory Open Access Journal |
| issn | 2081-8858 2391-6060 |
| language | English |
| last_indexed | 2024-03-11T18:12:47Z |
| publishDate | 2023-01-01 |
| publisher | Wrocław University of Science and Technology |
| record_format | Article |
| series | Operations Research and Decisions |
| spelling | doaj.art-fb260be4cd67448da272b036ac4a44dc2023-10-16T11:51:02ZengWrocław University of Science and TechnologyOperations Research and Decisions2081-88582391-60602023-01-01vol. 33no. 3105118171673541Combining Predictive Distributions of Electricity Prices. Does Minimizing the CRPS Lead to Optimal Decisions in Day-Ahead Bidding?Weronika Nitka0Rafał Weron1Wrocław University of Science and Technology, Wrocław, PolandWrocław University of Science and Technology, Wrocław, PolandProbabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article, we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we conduct an empirical study using hourly day-ahead electricity prices from the German EPEX market. We find that increasing the diversity of an ensemble can have a positive impact on accuracy. At the same time, the higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits, despite significantly more accurate predictions. (original abstract)https://ord.pwr.edu.pl/assets/papers_archive/ord2023vol33no3_7.pdf |
| spellingShingle | Weronika Nitka Rafał Weron Combining Predictive Distributions of Electricity Prices. Does Minimizing the CRPS Lead to Optimal Decisions in Day-Ahead Bidding? Operations Research and Decisions |
| title | Combining Predictive Distributions of Electricity Prices. Does Minimizing the CRPS Lead to Optimal Decisions in Day-Ahead Bidding? |
| title_full | Combining Predictive Distributions of Electricity Prices. Does Minimizing the CRPS Lead to Optimal Decisions in Day-Ahead Bidding? |
| title_fullStr | Combining Predictive Distributions of Electricity Prices. Does Minimizing the CRPS Lead to Optimal Decisions in Day-Ahead Bidding? |
| title_full_unstemmed | Combining Predictive Distributions of Electricity Prices. Does Minimizing the CRPS Lead to Optimal Decisions in Day-Ahead Bidding? |
| title_short | Combining Predictive Distributions of Electricity Prices. Does Minimizing the CRPS Lead to Optimal Decisions in Day-Ahead Bidding? |
| title_sort | combining predictive distributions of electricity prices does minimizing the crps lead to optimal decisions in day ahead bidding |
| url | https://ord.pwr.edu.pl/assets/papers_archive/ord2023vol33no3_7.pdf |
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