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Neighborhood Energy Modeling and Monitoring: A Case Study

Author

Listed:
  • Francesco Causone

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Rossano Scoccia

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Martina Pelle

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
    Institute for Renewable Energy, EURAC Research, 29100 Bolzano, Italy)

  • Paola Colombo

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Mario Motta

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

  • Sibilla Ferroni

    (Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy)

Abstract

Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited.

Suggested Citation

  • Francesco Causone & Rossano Scoccia & Martina Pelle & Paola Colombo & Mario Motta & Sibilla Ferroni, 2021. "Neighborhood Energy Modeling and Monitoring: A Case Study," Energies, MDPI, vol. 14(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3716-:d:579187
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    References listed on IDEAS

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    1. Martina Pelle & Francesco Causone & Laura Maturi & David Moser, 2023. "Opaque Coloured Building Integrated Photovoltaic (BIPV): A Review of Models and Simulation Frameworks for Performance Optimisation," Energies, MDPI, vol. 16(4), pages 1-20, February.
    2. Alberto Barbaresi & Mattia Ceccarelli & Giulia Menichetti & Daniele Torreggiani & Patrizia Tassinari & Marco Bovo, 2022. "Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need," Energies, MDPI, vol. 15(4), pages 1-16, February.

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