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Forecastability measures that describe the complexity of a site for deep learning wind predictions
dc.contributor.author | Manero Font, Jaume |
dc.contributor.author | Béjar Alonso, Javier |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Intel·ligència Artificial |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2021-06-03T11:10:00Z |
dc.date.available | 2021-06-03T11:10:00Z |
dc.date.issued | 2021-05-29 |
dc.identifier.citation | Manero, J.; Béjar, J. Forecastability measures that describe the complexity of a site for deep learning wind predictions. "Supercomputing frontiers and innovations", 29 Maig 2021, vol. 8, núm. 1, p. 8-27. |
dc.identifier.issn | 2313-8734 |
dc.identifier.uri | http://hdl.handle.net/2117/346575 |
dc.description.abstract | The application of deep learning to wind time series for multi-step prediction obtains good results at short horizons. The accuracy of a wind forecast is highly dependent on the specific structure of wind in the specific location, as many local features influence wind behaviour. The characterization of the complexity of a site for wind prediction is defined as forecastability or predictability and can be obtained from the inner structure of the meteorological time series observations from a site. We analyze the time series structure searching for properties that have a high correlation with the prediction result, properties that can create measures that have the potential to describe the forecastability of a site. The best measures will show a high correlation with the accuracy of the predictions. In this work, we analyze wind time series from 126,692 wind locations in the US, where we apply several deep learning methods first, and then we verify several forecastability descriptors with the accuracy deep learning results. We require High-Performance Computing (HPC) resources for this task as the deep learning algorithms have sensible resource requirements and are applied to a large set of data. The measures defined and explored in this work are based on several techniques that decompose or transform the wind time-series. By combining several of these measures, we can obtain better predictors of the site complexity, which will allow us to evaluate the future error of a prediction on this site. Forecastability measures can contribute to a wind site multi-dimensional description, becoming a valuable tool for wind resource analysts and wind forecasters. |
dc.format.extent | 20 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Time-series analysis |
dc.subject.lcsh | Wind forecasting |
dc.subject.other | Wind time series |
dc.subject.other | Deep learning |
dc.subject.other | CNN |
dc.subject.other | Convolutional networks |
dc.subject.other | Forecastability |
dc.title | Forecastability measures that describe the complexity of a site for deep learning wind predictions |
dc.type | Article |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Sèries temporals -- Anàlisi |
dc.subject.lemac | Vents -- Previsió |
dc.contributor.group | Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
dc.identifier.doi | 10.14529/jsfi210102 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://superfri.org/superfri/article/view/364 |
dc.rights.access | Open Access |
local.identifier.drac | 31784421 |
dc.description.version | Postprint (published version) |
local.citation.author | Manero, J.; Béjar, J. |
local.citation.publicationName | Supercomputing frontiers and innovations |
local.citation.volume | 8 |
local.citation.number | 1 |
local.citation.startingPage | 8 |
local.citation.endingPage | 27 |
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