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Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf Profile: Linearized PDF, Tagged PDF PDFMetadata: Objects: 3322 FreeObjects: 1 IncrementalUpdates: 1 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: false CenterWindow: false DisplayDocTitle: true NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseOutlines Language: EN Outlines: Item: Title: Improving energy distribution in collective self-consumption via XGBoost-based allocation coefficients prediction Children: Item: Title: 1 Introduction Item: Title: 2 Related work Children: Item: Title: 2.1 Dynamic allocation and sharing mechanisms Item: Title: 2.2 Machine learning applications in energy communities Item: Title: 2.3 Positioning and research gap Item: Title: 3 Foundations of energy communities Children: Item: Title: 3.1 Collective self-consumption: the Spain formulation Item: Title: 3.2 Mathematical formulation of energy distribution in CSC Item: Title: 3.3 Understanding allocation coefficients Item: Title: 4 Methodology Children: Item: Title: 4.1 Adoption of XGBoost Item: Title: 4.2 Ideal proportional heuristic framework Item: Title: 4.3 Learning to predict allocation coefficients Item: Title: 4.4 Framework scalability overview Item: Title: 4.5 Energy community performance metrics Children: Item: Title: Energy surplus Item: Title: Energy balance Item: Title: Surplus frequency index Item: Title: Average surplus per event Item: Title: Receiver utilization index Item: Title: Grid dependency ratio Item: Title: Energy balance accuracy Item: Title: Energy-weighted mean absolute error Item: Title: 4.6 Surplus compensation management Item: Title: 5 Case study: municipal energy community in catalonia Children: Item: Title: 5.1 Nominal case technical and data description Item: Title: 5.2 Economical considerations of the PV installation Item: Title: 6 Results and analysis Children: Item: Title: 6.1 XGBoost training and validation Item: Title: 6.2 Energy community performance analysis Children: Item: Title: 6.2.1 Nominal case analysis Item: Title: 6.2.2 Dynamic allocation case analysis Item: Title: 6.2.3 Comparative analysis Item: Title: 7 Conclusions and future work Item: Title: CRediT authorship contribution statement Item: Title: Declaration of competing interest Item: Title: Acknowledgements Item: Title: Appendix A. Nomenclature and symbols Children: Item: Title: A.1 Sets, indices, and dimensions Item: Title: A.2 Energy variables and CSC allocation Item: Title: A.3 Learning variables and feature definitions Item: Title: A.4 Heuristic target construction variables Item: Title: A.5 Performance metrics and economic variables Item: Title: A.6 Acronyms Item: Title: Data availability Item: Title: References Info: Title: Improving energy distribution in collective self-consumption via XGBoost-based allocation coefficients prediction Author: Sebastián Madrigal Subject: Applied Energy, 409 (2026) 127469. doi:10.1016/j.apenergy.2026.127469 Keywords: Energy communities,Collective self-consumption,Energy management,Machine learning for energy systems,XGBoost,Dynamic allocation Creator: Elsevier Producer: Acrobat Distiller 8.1.0 (Windows) CreationDate: Tue Feb 24 16:33:33 CET 2026 ModDate: Tue Feb 24 16:35:38 CET 2026 ID: 0xb368f1afc0a9e1972cb31ffc35844886, 0x86d151e24c9918cc8e60a7dd5bc836a9 Filters: 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