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Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

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  • Francesco Mancini

    (Department of Planning, Design and Technology of Architecture, Sapienza University of Rome, 00197 Rome, Italy)

  • Gianluigi Lo Basso

    (Department of Astronautics, Electrical Energy Engineering, Sapienza University of Rome, 00184 Rome, Italy)

  • Livio De Santoli

    (Department of Astronautics, Electrical Energy Engineering, Sapienza University of Rome, 00184 Rome, Italy)

Abstract

This work shows the outcomes of a research activity aimed at the energy characterization of residential users. Specifically, by data analysis related to the real energy consumption of sample buildings, the flexible loads amount has been identified so as to investigate on the opportunity to implement a demand/response (DR) program. The most meaningful input data have been collected by an on-line questionnaire created within an Excel spreadsheet allowing one to simulate and compare the calculations with the actual dwellings’ consumption; 412 questionnaires have been used as statistical sample and simulations have been performed based on single-zone dynamic model. Additionally, once the energy consumptions have been sorted by the different services, reference key performance indicators (KPIs) have been also calculated normalising those ones by people and house floor surface. From data analysis, it emerges how the Italian residential users are not very electrified. Furthermore, the flexible loads are low and, implementing minor maintenance interventions, the potential of flexibility can decrease up to 20%. For that reason, the current research can be further developed by investigating on suitable flexibility extensions as well as on the automation system requirements which is needed managing the flexible loads.

Suggested Citation

  • Francesco Mancini & Gianluigi Lo Basso & Livio De Santoli, 2019. "Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey," Energies, MDPI, vol. 12(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2055-:d:235300
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    References listed on IDEAS

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    Cited by:

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    2. Francesco Mancini & Benedetto Nastasi, 2019. "Energy Retrofitting Effects on the Energy Flexibility of Dwellings," Energies, MDPI, vol. 12(14), pages 1-19, July.
    3. Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
    4. Francesco Mancini & Gianluigi Lo Basso, 2020. "How Climate Change Affects the Building Energy Consumptions Due to Cooling, Heating, and Electricity Demands of Italian Residential Sector," Energies, MDPI, vol. 13(2), pages 1-24, January.
    5. Francesco Mancini & Sabrina Romano & Gianluigi Lo Basso & Jacopo Cimaglia & Livio de Santoli, 2020. "How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy," Energies, MDPI, vol. 13(13), pages 1-25, July.
    6. Christian Pfeiffer & Markus Puchegger & Claudia Maier & Ina V. Tomaschitz & Thomas P. Kremsner & Lukas Gnam, 2020. "A Case Study of Socially-Accepted Potentials for the Use of End User Flexibility by Home Energy Management Systems," Sustainability, MDPI, vol. 13(1), pages 1-19, December.
    7. Francesco Mancini & Jacopo Cimaglia & Gianluigi Lo Basso & Sabrina Romano, 2021. "Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study," Energies, MDPI, vol. 14(11), pages 1-21, May.
    8. Francesco Mancini & Gianluigi Lo Basso & Livio de Santoli, 2019. "Energy Use in Residential Buildings: Impact of Building Automation Control Systems on Energy Performance and Flexibility," Energies, MDPI, vol. 12(15), pages 1-21, July.
    9. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
    10. Francesco Mancini & Benedetto Nastasi, 2020. "Solar Energy Data Analytics: PV Deployment and Land Use," Energies, MDPI, vol. 13(2), pages 1-18, January.

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