Articles producció científicaEnginyeria Mecànica

A hybrid data-driven Co-simulation approach for enhanced integrations of renewables and thermal storage in building district energy systems

  • Dades identificatives

    Identificador:  imarina:9452262
    Autors:  Elomari, Youssef; Aspetakis, Giorgos; Mateu, Carles; Shobo, Adedamola; Boer, Dieter; Marin-Genesca, M; Wang, Qian
    Resum:
    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.
  • Altres:

    Autor segons l'article: Elomari, Youssef; Aspetakis, Giorgos; Mateu, Carles; Shobo, Adedamola; Boer, Dieter; Marin-Genesca, M; Wang, Qian
    Departament: Enginyeria Mecànica
    Autor/s de la URV: Boer, Dieter-Thomas / Marín Genescà, Marc
    Paraules clau: Co-simulation framework; Deep reinforcement learning; District energy system; Heating system; Multi-objective optimization; Optimization; Plants; Pum; Rule-based contro; Rule-based control
    Resum: 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.
    Àrees temàtiques: 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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: dieter.boer@urv.cat; marc.marin@urv.cat
    Data d'alta del registre: 2025-04-30
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.sciencedirect.com/science/article/pii/S2352710225006424?via%3Dihub
    Referència a l'article segons font original: Journal Of Building Engineering. 104 112405-
    Referència 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
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.1016/j.jobe.2025.112405
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2025
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Architecture,Building and Construction,Civil and Structural Engineering,Construction & Building Technology,Engineering, Civil,Mechanics of Materials,Safety, Risk, Reliability and Quality
    Co-simulation framework
    Deep reinforcement learning
    District energy system
    Heating system
    Multi-objective optimization
    Optimization
    Plants
    Pum
    Rule-based contro
    Rule-based control
    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
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