Probabilistic multivariate electricity price forecasting using implicit generative ensemble post-processing
| Autor: | Florian Steinke, Tim Janke |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Multivariate statistics Computer science Electricity price forecasting Econometrics (econ.EM) 0211 other engineering and technologies Weather forecasting Machine Learning (stat.ML) 02 engineering and technology computer.software_genre Machine learning Statistics - Applications 01 natural sciences FOS: Economics and business 010104 statistics & probability Statistics - Machine Learning Applications (stat.AP) 021108 energy 0101 mathematics Representation (mathematics) Economics - Econometrics Statistical Finance (q-fin.ST) business.industry Probabilistic logic Quantitative Finance - Statistical Finance Data set Generative model Risk Management (q-fin.RM) Artificial intelligence business computer Quantitative Finance - Risk Management Optimal decision |
| Zdroj: | 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). |
| DOI: | 10.1109/pmaps47429.2020.9183687 |
| Popis: | The reliable estimation of forecast uncertainties is crucial for risk-sensitive optimal decision making. In this paper, we propose implicit generative ensemble post-processing, a novel framework for multivariate probabilistic electricity price forecasting. We use a likelihood-free implicit generative model based on an ensemble of point forecasting models to generate multivariate electricity price scenarios with a coherent dependency structure as a representation of the joint predictive distribution. Our ensemble post-processing method outperforms well-established model combination benchmarks. This is demonstrated on a data set from the German day-ahead market. As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks. Comment: To be presented at the 16th International Conference on Probabilistic Methods Applied to Power Systems 2020 (PMAPS 2020) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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