Enhancing wind power forecasting and ramp detection using long short‐term memory networks and the swinging door algorithm

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dc.contributor.authorPandit, Ravi
dc.contributor.authorMu, Shikun
dc.contributor.authorAstolfi, Davide
dc.date.accessioned2025-02-20T10:19:28Z
dc.date.available2025-02-20T10:19:28Z
dc.date.freetoread2025-02-20
dc.date.issued2025
dc.date.pubOnline2025-01-28
dc.description.abstractAccurate prediction of short‐term wind power ramps is essential for effective smart grid management. This study introduces the swinging door algorithm for ramp detection, which outperforms traditional methods by precisely identifying ramp events. Additionally, a long short‐term memory (LSTM) network is evaluated against established models such as support vector machines, artificial neural networks, convex multi‐task feature learning, and random forest for wind power ramp forecasting. The LSTM model demonstrates superior performance, achieving the lowest weighted mean absolute percentage error of 8.36% and normalized root mean squared error of 0.60, alongside the highest R‐squared (R2) value of 0.73, indicating strong predictive accuracy and correlation with observed data. Furthermore, the combined swinging door algorithm‐LSTM framework improved ramp event detection by 15% compared to traditional methods, showcasing its robustness in capturing both mild and extreme ramp events. This research underlines LSTM's effectiveness in wind power forecasting, marking a notable advancement in prediction methodologies. By illustrating the strengths of LSTM and swinging door algorithm, the study contributes to the refinement of prediction models for smart grid applications, highlighting their potential to transform wind power ramp prediction and detection.
dc.description.journalNameIET Renewable Power Generation
dc.identifier.citationPandit R, Mu S, Astolfi D. (2025) Enhancing wind power forecasting and ramp detection using long short‐term memory networks and the swinging door algorithm. IET Renewable Power Generation, Volume 19, 2025, Article number e70002
dc.identifier.eissn1752-1424
dc.identifier.elementsID563507
dc.identifier.issn1752-1416
dc.identifier.paperNoe70002
dc.identifier.urihttps://doi.org/10.1049/rpg2.70002
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23497
dc.identifier.volumeNo19
dc.languageEnglish
dc.language.isoen
dc.publisherInstitution of Engineering and Technology (IET)
dc.publisher.urihttps://ietresearch.onlinelibrary.wiley.com/doi/10.1049/rpg2.70002
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectwind turbines
dc.subjectwind power
dc.subjectwind
dc.subject40 Engineering
dc.subject4008 Electrical Engineering
dc.subject4009 Electronics, Sensors and Digital Hardware
dc.subject4011 Environmental Engineering
dc.subject7 Affordable and Clean Energy
dc.subjectEnergy
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4011 Environmental engineering
dc.titleEnhancing wind power forecasting and ramp detection using long short‐term memory networks and the swinging door algorithm
dc.typeArticle
dc.type.subtypeArticle
dcterms.dateAccepted2025-01-11

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