Empirical prediction intervals improve energy forecasting
Open accessAbstract
Significance: While many forecasters are moving toward generating probabilistic predictions, energy forecasts typically still consist of point projections and scenarios without associated probabilities. Empirical density forecasting methods provide a probabilistic amendment to existing point forecasts. Here we lay the groundwork for evaluating the performance of these methods in the data-scarce setting of long-term forecasts. Results can give policy analysts and other users confidence in estimating forecast uncertainties with empirical methods.
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Hybrid physics-plus-ML techniques can help improve short-to medium-term forecasts, and are also necessary for ML to contribute to longer-term forecasts since weather distributions shift over time.
“Such techniques can help improve short-to medium-term forecasts, and are also necessary for ML to contribute to longer-term (e.g., year-scale) forecasts since weather distributions shift over time [404].”
From Tackling climate change with machine learning (2023) · cites this paper as [404]
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Tackling climate change with machine learning
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Self-citation
- Authors
- Kaack, L. H., Apt, J., Morgan, M. G., & McSharry, P.
- Affiliations
- Carnegie Mellon University (US)
- University of Oxford (GB)
- Corresponding author
- Kaack, Lynn H.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-07-31
- Volume
- 114
- Issue
- 33
- Pages
- 8752-8757
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5565406
- OpenAlex
- W2740233740
- Cited by
- 43
- References
- 35
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 4 · 2023: 7 · 2022: 5 · 2021: 4 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 1 · 2012: 1
- FWCI
- 2.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- OPUS 4 (Zuse Institute Berlin) (submittedVersion)
- Funders
- National Science Foundation (SES-0949710)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- energy forecasting, prediction intervals, uncertainty quantification, continuous ranked probability score, out-of-sample forecasting, nonparametric density estimation, probabilistic forecasting
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