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Part 1: Document Description
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Citation |
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Title: |
Enhanced spatio-temporal electric load forecasts using less data with active deep learning |
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Identification Number: |
doi:10.7910/DVN/3VYYET |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2022-03-31 |
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Version: |
3 |
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Bibliographic Citation: |
Aryandoust, Arsam, 2022, "Enhanced spatio-temporal electric load forecasts using less data with active deep learning", https://doi.org/10.7910/DVN/3VYYET, Harvard Dataverse, V3 |
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Citation |
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Title: |
Enhanced spatio-temporal electric load forecasts using less data with active deep learning |
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Identification Number: |
doi:10.7910/DVN/3VYYET |
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Authoring Entity: |
Aryandoust, Arsam (ETH Zürich) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Aryandoust, Arsam |
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Depositor: |
Aryandoust, Arsam |
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Date of Deposit: |
2022-03-31 |
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Holdings Information: |
https://doi.org/10.7910/DVN/3VYYET |
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Study Scope |
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Keywords: |
Computer and Information Science, Earth and Environmental Sciences, Mathematical Sciences |
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Abstract: |
Aryandoust, A., Patt, A. & Pfenninger, S. Enhanced spatio-temporal electric load forecasts using less data with active deep learning. Nature Machine Intelligence 4, 977–991 (2022). https://doi.org/10.1038/s42256-022-00552-x |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Label: |
dataset.zip |
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Text: |
contains two public folders: data and results |
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Notes: |
application/zip |