Articles producció científica> Enginyeria Mecànica

A data-driven framework for designing a renewable energy community based on the integration of machine learning model with life cycle assessment and life cycle cost parameters

  • Identification data

    Identifier: imarina:9366486
  • Authors:

    Elomari, Y
    Mateu, C
    Marín-Genescà, M
    Boer, D
    Applied Energy
    10.1016/j.apenergy.2024.122619
    Applied Energy. 358 122619-
  • Others:

    Author, as appears in the article.: Elomari, Y; Mateu, C; Marín-Genescà, M; Boer, D
    Department: Enginyeria Mecànica
    URV's Author/s: Boer, Dieter-Thomas / Elomari, Youssef / Marín Genescà, Marc
    Keywords: Systems Performance Optimization Multi-objective optimization Multi-criteria decision making Multi -objective optimization Multi -criteria decision making Machine learning Life cycle cost Life cycle assessment Generation Energy community
    Abstract: This research paper presents a data-driven framework for design optimization of renewable energy communities (RECs) in the residential sector, considering both techno-economic challenges and environmental impact. The study's focus is to determine suitable sizes for photovoltaic systems, wind turbines, and battery electrical energy systems by evaluating energy, economic, and environmental criteria. To achieve this, we develop a data-driven model that incorporates Homer Pro and an in-house tool developed in Python programming language that integrates a machine learning algorithm, life cycle cost (LCC), life cycle assessment (LCA) calculations of the REC model. Furthermore, a multi-objective optimization model is established to minimize the LCC and LCA parameters while maximizing green energy use. Moreover, a multi-criteria decision-making approach based on Weighted Sum Model (WSM) is proposed to help the stakeholders to see beyond the selection criteria based on LCC and LCA to choose the most appropriate scenario optimal solution for the desired energy community and interpret the effect of various economic parameters on the sustainable performance of REC. The framework application is illustrated through a case study for the optimal design of REC for a residential community in Tarragona, Spain, consisting of 100 buildings. The results revealed a substantial improvement in economic , environmental benefits for designing REC, the optimal minimum cost solution with a levelized cost of energy (LCOE = 0.044 $/kWh) and a payback period of 7.1 years with an LCOE reduction of 85.04% compared to the base case. The minimum impact with an LCOE = 0.220 $/kWh and a payback period of 12.5 years with a reduction in environmental impact of 54.59% compared to the base case. Overall, the developed data -driven provides policy decision -making with an evaluation of REC in the residential sector.
    Thematic Areas: Renewable energy, sustainability and the environment Química Nuclear energy and engineering Mechanical engineering Materiais Matemática / probabilidade e estatística Management, monitoring, policy and law Interdisciplinar Geociências General energy Fuel technology Farmacia Engineering, chemical Engenharias iv Engenharias iii Engenharias ii Engenharias i Energy engineering and power technology Energy (miscellaneous) Energy (all) Energy & fuels Economia Civil and structural engineering Ciências biológicas iii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Building and construction Biotecnología Biodiversidade Arquitetura, urbanismo e design
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: youssef.elomari@urv.cat marc.marin@urv.cat youssef.elomari@urv.cat dieter.boer@urv.cat
    Author identifier: 0000-0002-7204-4526 0000-0002-5532-6409
    Record's date: 2024-07-20
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S0306261924000023
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Applied Energy. 358 122619-
    APA: Elomari, Y; Mateu, C; Marín-Genescà, M; Boer, D (2024). A data-driven framework for designing a renewable energy community based on the integration of machine learning model with life cycle assessment and life cycle cost parameters. Applied Energy, 358(), 122619-. DOI: 10.1016/j.apenergy.2024.122619
    Article's DOI: 10.1016/j.apenergy.2024.122619
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2024
    Publication Type: Journal Publications
  • Keywords:

    Building and Construction,Civil and Structural Engineering,Energy & Fuels,Energy (Miscellaneous),Energy Engineering and Power Technology,Engineering, Chemical,Fuel Technology,Management, Monitoring, Policy and Law,Mechanical Engineering,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment
    Systems
    Performance
    Optimization
    Multi-objective optimization
    Multi-criteria decision making
    Multi -objective optimization
    Multi -criteria decision making
    Machine learning
    Life cycle cost
    Life cycle assessment
    Generation
    Energy community
    Renewable energy, sustainability and the environment
    Química
    Nuclear energy and engineering
    Mechanical engineering
    Materiais
    Matemática / probabilidade e estatística
    Management, monitoring, policy and law
    Interdisciplinar
    Geociências
    General energy
    Fuel technology
    Farmacia
    Engineering, chemical
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Engenharias i
    Energy engineering and power technology
    Energy (miscellaneous)
    Energy (all)
    Energy & fuels
    Economia
    Civil and structural engineering
    Ciências biológicas iii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Ciência da computação
    Building and construction
    Biotecnología
    Biodiversidade
    Arquitetura, urbanismo e design
  • Documents:

  • Cerca a google

    Search to google scholar