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Sub-Hourly Load Forecasting for Community-Level Flexible Appliance Management

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dc.contributor.author Menos-Aikateriniadis, Christoforos
dc.contributor.author Akarepis, Andreas
dc.contributor.author Kokos, Isidoros
dc.contributor.author Georgilakis, Pavlos
dc.date.accessioned 2024-09-13T13:52:42Z
dc.date.available 2024-09-13T13:52:42Z
dc.identifier.issn 2161-4393 el
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/60212
dc.identifier.uri http://dx.doi.org/10.26240/heal.ntua.27908
dc.rights Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα *
dc.rights.uri http://creativecommons.org/licenses/by-nd/3.0/gr/ *
dc.subject Very Short-Term Load Forecasting en
dc.subject LSTM en
dc.subject Prophet en
dc.subject N-BEATS en
dc.subject Aggregate Flexible Load en
dc.title Sub-Hourly Load Forecasting for Community-Level Flexible Appliance Management en
heal.type conferenceItem
heal.classification Machine Learning el
heal.classification Smart Grids en
heal.classification Forecasting en
heal.classification Energy flexibility en
heal.language en
heal.access free
heal.recordProvider ntua el
heal.publicationDate 2024-09-09
heal.bibliographicCitation C. Menos-Aikateriniadis, A. Akarepis, I. Kokos and P. S. Georgilakis, "Sub-Hourly Load Forecasting for Community-Level Flexible Appliance Management," 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, pp. 1-8 en
heal.abstract Very Short-Term Load Forecasting (VSTLF) at a residential level constitutes a challenge mainly due to the highly volatile end-user consumption patterns as well as data integrity issues that jeopardize the quality of collected data. VSTLF can enable improved flexibility quantification and more efficient residential Demand-Side Management (DSM) for load aggregators, not only on a consumer level but also on an aggregated appliance level. Inspired by the ongoing research debate between statistical-based and data-driven models for VSTLF, this work investigates the performance of a Long Short-Term Memory (LSTM) model with Feed-Forward Error Correction (FFEC), the widely-used XGBoost method and the State-of-the-Art (SoA) models N-BEATS and Prophet. Models were trained and tested on 15-minute aggregate Electric Vehicle (EV) and Air Conditioning (A/C) loads, using real measurements by the Pecan Street dataset. In contrast to common research practices, this work considers solely temporal features and historical consumption for training the models. Results indicate that firstly LSTM with FFEC and secondly XGBoost models capture more accurately sub-hourly load dependencies for both EVs and A/Cs, showcasing their ability to generalize efficiently across different appliances and sparse datasets. N-BEATS model performs adequately well but it cannot outperform the aforementioned models, while Prophet fails to capture very short term dependencies in the data. en
heal.sponsor This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska Curie grant agreement No 955422. en
heal.publisher IEEE en
heal.fullTextAvailability false
heal.conferenceName 2024 International Joint Conference on Neural Networks (IJCNN) en
heal.conferenceItemType full paper
dc.identifier.doi 10.1109/IJCNN60899.2024.10649928 el


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