In Situ Measurements of Energy Consumption and Indoor Environmental Quality of a Pre-Retrofitted Student Dormitory in Athens
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- Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
- Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
- Hammarsten, Stig, 1987. "A critical appraisal of energy-signature models," Applied Energy, Elsevier, vol. 26(2), pages 97-110.
- Ferenc Szodrai & Ferenc Kalmár, 2019. "Simulation of Temperature Distribution on the Face Skin in Case of Advanced Personalized Ventilation System," Energies, MDPI, vol. 12(7), pages 1-11, March.
- Annarita Ferrante & Giovanni Mochi & Giorgia Predari & Lorenzo Badini & Anastasia Fotopoulou & Riccardo Gulli & Giovanni Semprini, 2018. "A European Project for Safer and Energy Efficient Buildings: Pro-GET-onE (Proactive Synergy of inteGrated Efficient Technologies on Buildings’ Envelopes)," Sustainability, MDPI, vol. 10(3), pages 1-26, March.
- Anti Hamburg & Targo Kalamees, 2018. "The Influence of Energy Renovation on the Change of Indoor Temperature and Energy Use," Energies, MDPI, vol. 11(11), pages 1-15, November.
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- Isidro Calvo & Aitana Espin & Jose Miguel Gil-García & Pablo Fernández Bustamante & Oscar Barambones & Estibaliz Apiñaniz, 2022. "Scalable IoT Architecture for Monitoring IEQ Conditions in Public and Private Buildings," Energies, MDPI, vol. 15(6), pages 1-23, March.
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Keywords
energy efficiency; student dormitories; Indoor Environmental Quality (IEQ); Pro-GET-onE H2020; in situ measurements; monitoring measurements; energy signature;All these keywords.
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