Daily soil temperatures predictions for various climates in United States using data-driven model
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 12 - Engineering
- Output identifier
- 32180507
- Type
- D - Journal article
- DOI
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10.1016/j.energy.2018.07.004
- Title of journal
- Energy
- Article number
- -
- First page
- 430
- Volume
- 160
- Issue
- -
- ISSN
- 0360-5442
- Open access status
- Deposit exception
- Month of publication
- July
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents an innovative data-driven model for predicting daily soil temperatures on a continental scale. The new model considers daily temperature variations as superposition of annual average and daily amplitude predictions. The innovative method of classifying training ground temperature data based on an analytical model greatly improved the prediction accuracy compared to conventional methods. It underpinned the subsequent award of a Natural Science Foundation of Hubei Province (Grant No. 2019CFB140) to investigate and demonstrate the effect of ground temperature predictions accuracy on geothermal energy system cost, efficiency, and its environmental impact.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
