Quantum long short-term memory (QLSTM) vs. classical LSTM in time series forecasting: a comparative study in solar power forecasting
Rattachement africain : pk, sa, se. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. The primary objective is to evaluate the potential advantages of QLSTMs, leveraging their exponential representational capabilities, in capturing the intricate spatiotemporal patterns inherent in renewable energy data. Through controlled experiments on real-world photovoltaic datasets, our findings reveal promising improvements offered by QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical results demonstrate QLSTM’s potential to swiftly assimilate complex time series relationships, enabled by quantum phenomena like superposition. However, realizing QLSTM’s full capabilities necessitates further research into model validation across diverse conditions, systematic hyperparameter optimization, hardware noise resilience, and applications to correlated renewable forecasting problems. With continued progress, quantum machine learning can offer a paradigm shift in renewable energy time series prediction, potentially ushering in an era of unprecedented accuracy and reliability in solar power forecasting worldwide. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM models while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study illustrates a promising trajectory for quantum learning in renewable forecasting.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Quantum long short-term memory (QLSTM) vs. classical LSTM in time series forecasting: a comparative study in solar power forecasting
- Date Crossref
- 09/10/2024
- Éditeur
- Frontiers Media SA
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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National University of Sciences and Technology pays non établi dans la noticeUniversité ou école supérieure
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Ghulam Ishaq Khan Institute of Engineering Sciences and Technology pays non établi dans la noticeUniversité ou école supérieure
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King Abdulaziz University Department of Electrical and Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
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Uppsala University Department of Physics and Astronomy pays non établi dans la noticeUniversité ou école supérieure
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School of Electrical Engineering and Computer Science pays non établi dans la noticeUniversité ou école supérieure
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College of Aeronautical Engineering pays non établi dans la noticeUniversité ou école supérieure
National University of Sciences and Technology, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology et Department of Electrical and Computer Engineering — King Abdulaziz University, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.