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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