Repositori institucional URV
| Autor/es de la URV: | Boer, Dieter-Thomas / Marín Genescà, Marc |
| Autor según el artículo: | Elomari, Youssef; Aspetakis, Giorgos; Mateu, Carles; Shobo, Adedamola; Boer, Dieter; Marin-Genesca, M; Wang, Qian |
| Direcció de correo del autor: | dieter.boer@urv.cat marc.marin@urv.cat |
| Identificador del autor: | 0000-0002-5532-6409 0000-0002-7204-4526 |
| Año de publicación de la revista: | 2025 |
| Tipo de publicación: | Journal Publications |
| Referencia de l'ítem segons les normes APA: | Elomari, Youssef; Aspetakis, Giorgos; Mateu, Carles; Shobo, Adedamola; Boer, Dieter; Marin-Genesca, M; Wang, Qian (2025). A hybrid data-driven Co-simulation approach for enhanced integrations of renewables and thermal storage in building district energy systems. Journal Of Building Engineering, 104(), 112405-. DOI: 10.1016/j.jobe.2025.112405 |
| Referencia al articulo segun fuente origial: | Journal Of Building Engineering. 104 112405- |
| Resumen: | Increasing the share of renewables is crucial for accelerating the sustainable transitions of modern building and district heating systems. This study develops a hybrid co-simulation framework, integrating a Python-based model with an established district energy system (DES) TRNSYS model, to optimize the design and control of on-site renewables such as photovoltaic panels (PV), solar thermal collectors, a water-to-water heat pump, seasonal thermal storage, a domestic hot water tank, and auxiliary heaters. The methodology combines diverse simulation tools and datadriven control sequences, enabling interaction across system components for enhanced energy efficiency and performance. The findings indicate that the optimized framework reduces net present cost by approximately 14 % and environmental impacts by 11 %. The data-driven controls further minimized temperature deviations significantly better than traditional Rule-Based Controls, achieving nearly optimal comfort levels with minimal environmental impact. The developed co-simulation enhances energy efficiency and intelligent controls in building applications, minimizes environmental impacts, and effectively covers the energy demand in building and districts (building clusters). These findings highlight the essential role of advanced hybrid co-simulation frameworks in improving DH system design and control, emphasizing their potential for sustainable urban energy transitions. |
| DOI del artículo: | 10.1016/j.jobe.2025.112405 |
| Enlace a la fuente original: | https://www.sciencedirect.com/science/article/pii/S2352710225006424?via%3Dihub |
| Versión del articulo depositado: | info:eu-repo/semantics/publishedVersion |
| Acceso a la licencia de uso: | https://creativecommons.org/licenses/by/3.0/es/ |
| Departamento: | Enginyeria Mecànica |
| URL Documento de licencia: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Áreas temáticas: | Architecture Arquitetura, urbanismo e design Building and construction Civil and structural engineering Construction & building technology Engenharias i Engineering, civil Mechanics of materials Safety, risk, reliability and quality |
| Palabras clave: | Co-simulation framework Deep reinforcement learning District energy system Heating system Multi-objective optimization Optimization Plants Pum Rule-based contro Rule-based control |
| Entidad: | Universitat Rovira i Virgili |
| Fecha de alta del registro: | 2025-04-30 |
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| DocumentPrincipal | DocumentPrincipal | application/pdf |
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