Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/137836
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dc.contributor.author | Karimi-Arpanahi, S. | - |
dc.contributor.author | Pourmousavi, S.A. | - |
dc.contributor.author | Mahdavi, N. | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Patterns, 2023; 4(4):100708-1-100708-16 | - |
dc.identifier.issn | 2666-3899 | - |
dc.identifier.issn | 2666-3899 | - |
dc.identifier.uri | https://hdl.handle.net/2440/137836 | - |
dc.description | Published: March 24, 2023 | - |
dc.description.abstract | Decision-making in the power systems domain often relies on predictions of renewable generation. While sophisticated forecasting methods have been developed to improve the accuracy of such predictions, their accuracy is limited by the inherent predictability of the data used. However, the predictability of time series data cannot be measured by existing prediction techniques. This important measure has been overlooked by researchers and practitioners in the power systems domain. In this paper, we systematically assess the suitability of various predictability measures for renewable generation time series data, revealing the best method and providing instructions for tuning it. Using real-world examples, we then illustrate how predictability could save end users and investors millions of dollars in the electricity sector. | - |
dc.description.statementofresponsibility | Sahand Karimi-Arpanahi, S. Ali Pourmousavi, and Nariman Mahdavi | - |
dc.language.iso | en | - |
dc.publisher | Elsevier (Cell Press) | - |
dc.rights | © 2023 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | - |
dc.source.uri | http://dx.doi.org/10.1016/j.patter.2023.100708 | - |
dc.subject | PV generation time series | - |
dc.subject | electricity market analysis | - |
dc.subject | generation predictability | - |
dc.subject | power systems data analysis | - |
dc.subject | power systems decision making | - |
dc.subject | renewable generation forecasting | - |
dc.subject | time series predictability | - |
dc.subject | weighted permutation entropy | - |
dc.title | Quantifying the predictability of renewable energy data for improving power systems decision-making | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.patter.2023.100708 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Karimi-Arpanahi, S. [0000-0002-7607-6314] | - |
dc.identifier.orcid | Pourmousavi, S.A. [0000-0003-1115-4200] | - |
Appears in Collections: | Electrical and Electronic Engineering publications |
Files in This Item:
File | Description | Size | Format | |
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hdl_137836.pdf | Published version | 23.85 MB | Adobe PDF | View/Open |
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