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Open Tool for Automated Development of Renewable Energy Communities: Artificial Intelligence and Machine Learning Techniques for Methodological Approach

Author

Listed:
  • Giuseppe Piras

    (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Roma, Italy)

  • Francesco Muzi

    (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Roma, Italy)

  • Zahra Ziran

    (Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Roma, Italy)

Abstract

The architecture, engineering, construction, and operations (AECO) sector exerts a considerable influence on energy consumption and CO 2 emissions released into the atmosphere, making a notable contribution to climate change. It is therefore imperative that energy efficiency in buildings is prioritized in order to reduce environmental impacts and meet the targets set out in the European 2030 Agenda. In this context, renewable energy communities (RECs) have the potential to play an important role, promoting the use of renewable energy at the local level, optimizing energy management, and reducing consumption by sharing resources and advanced technologies. This paper introduces an open tool (OT) designed for the configuration of energy systems dedicated to RECs. The OT considers several inputs, including thermal and electrical loads, energy consumption, the type of building, surface area, and population size. The OT employs artificial intelligence (AI) algorithms and machine learning (ML) techniques to generate forecast optimized scenarios for the sizing of photovoltaic systems, thermal, and electrical storage, and the estimation of CO 2 emission reductions. The OT features a user-friendly interface, enabling even non-experts to obtain comprehensive configurations for RECs, aiming to accelerate the transition toward sustainable and efficient district energy systems, driving positive environmental impact and fostering a greener future for communities and cities.

Suggested Citation

  • Giuseppe Piras & Francesco Muzi & Zahra Ziran, 2024. "Open Tool for Automated Development of Renewable Energy Communities: Artificial Intelligence and Machine Learning Techniques for Methodological Approach," Energies, MDPI, vol. 17(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5726-:d:1522027
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    References listed on IDEAS

    as
    1. Giuseppe Piras & Sofia Agostinelli & Francesco Muzi, 2024. "Digital Twin Framework for Built Environment: A Review of Key Enablers," Energies, MDPI, vol. 17(2), pages 1-27, January.
    2. Tiwari, Aviral Kumar & Trabelsi, Nader & Abakah, Emmanuel Joel Aikins & Nasreen, Samia & Lee, Chien-Chiang, 2023. "An empirical analysis of the dynamic relationship between clean and dirty energy markets," Energy Economics, Elsevier, vol. 124(C).
    3. Liao, Wei & Xiao, Fu & Li, Yanxue & Zhang, Hanbei & Peng, Jinqing, 2024. "A comparative study of demand-side energy management strategies for building integrated photovoltaics-battery and electric vehicles (EVs) in diversified building communities," Applied Energy, Elsevier, vol. 361(C).
    4. Sunyong Kim & Hyuk Lim, 2018. "Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings," Energies, MDPI, vol. 11(8), pages 1-19, August.
    5. Rocha, Helder R.O. & Fiorotti, Rodrigo & Louzada, Danilo M. & Silvestre, Leonardo J. & Celeste, Wanderley C. & Silva, Jair A.L., 2024. "Net Zero Energy cost Building system design based on Artificial Intelligence," Applied Energy, Elsevier, vol. 355(C).
    6. Giuseppe Piras & Francesco Muzi, 2024. "Energy Transition: Semi-Automatic BIM Tool Approach for Elevating Sustainability in the Maputo Natural History Museum," Energies, MDPI, vol. 17(4), pages 1-22, February.
    7. Vallati, Andrea & Di Matteo, Miriam & Sundararajan, Mukund & Muzi, Francesco & Fiorini, Costanza Vittoria, 2024. "Development and optimization of an energy saving strategy for social housing applications by water source-heat pump integrating photovoltaic-thermal panels," Energy, Elsevier, vol. 301(C).
    8. Giuseppe Piras & Adriana Scarlet Sferra, 2024. "Environmental Product Declarations as a Data Source for the Assessment of Environmental Impacts during the Use Phase of Photovoltaic Modules: Critical Issues and Potential," Energies, MDPI, vol. 17(2), pages 1-19, January.
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