Journal article
The need for better statistical testing in data-driven energy technology modeling
- Abstract:
- Technology modeling is a vital part of developing and understanding energy system scenarios and policy, but it is challenging due to data limitations, deep uncertainty, and the complex social and technological dynamics involved in the evolution of energy systems. These difficulties are often compounded by unsound technology forecasting practice, including overfitting, data selection bias, and ad hoc assumptions, leading to unreliable conclusions. We flag several cases where this has been problematic and analyze in detail a recent model for predicting the pace of solar photovoltaic and wind energy deployment. We discuss general takeaways and provide suggestions for how statistical testing should be conducted to avoid such problems in the future and to quantify the reliability of forecasts.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 8.1MB, Terms of use)
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- Publisher copy:
- 10.1016/j.joule.2024.07.016
Authors
- Publisher:
- Cell Press
- Journal:
- Joule More from this journal
- Volume:
- 8
- Issue:
- 9
- Pages:
- 2453-2466
- Publication date:
- 2024-08-26
- Acceptance date:
- 2024-07-23
- DOI:
- EISSN:
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2542-4351
- Language:
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English
- Keywords:
- Pubs id:
-
2017992
- Local pid:
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pubs:2017992
- Deposit date:
-
2024-07-23
Terms of use
- Copyright holder:
- Baumgärtner et al.
- Copyright date:
- 2024
- Rights statement:
- © 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
- Licence:
- CC Attribution (CC BY)
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