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A Novel Strategy for Converting Conventional Structures into Net-Zero-Energy Buildings without Destruction

Author

Listed:
  • Hisham Alghamdi

    (Electrical Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia)

  • Aníbal Alviz-Meza

    (Grupo de Investigación en Deterioro de Materiales, Transición Energética y Ciencia de datos DANT3, Facultad de Ingenieria y Urbanismo, Universidad Señor de Sipán, Km 5 Via Pimentel, Chiclayo 14001, Peru)

Abstract

The majority of energy consumption is attributed to buildings. Buildings designed with environmentally sustainable features have the potential to reduce energy consumption. The demolition of ecologically detrimental structures incurs expenses and damages the natural environment. The act of constructing models for the purpose of destruction was deemed superfluous. The replication of the structural model was accompanied by a modification of the design, and a variety of tactics were employed. The proposed upgrades for the building include the installation of new windows, incorporation of greenery on the walls and roof, implementation of insulation, and integration of solar panels in a four-story residential building in Najran, Saudi Arabia. Simultaneously installing insulation prior to changing windows will ensure that the energy consumption of the building, green wall, or green roof will remain unaffected. The installation of solar panels on the walls and top roof of a structure has the potential to generate a monthly electricity output up to two times greater than the structure’s consumption. The spas can be heated on a daily basis by substituting the heating system with solar collectors. The implementation of sustainable building practices has resulted in a significant reduction in energy consumption. Specifically, electricity, gas, heating, and cooling consumption decreased by 11%, 85%, 28%, and 83%, respectively.

Suggested Citation

  • Hisham Alghamdi & Aníbal Alviz-Meza, 2023. "A Novel Strategy for Converting Conventional Structures into Net-Zero-Energy Buildings without Destruction," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11229-:d:1197144
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    References listed on IDEAS

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    1. Nijegorodov, N. & Adedoyin, J.A. & Devan, K.R.S., 1997. "A new analytical-empirical model for the instantaneous diffuse radiation and experimental investigation of its validity," Renewable Energy, Elsevier, vol. 11(3), pages 341-350.
    2. Behrouz Pirouz & Stefania Anna Palermo & Mario Maiolo & Natale Arcuri & Patrizia Piro, 2020. "Decreasing Water Footprint of Electricity and Heat by Extensive Green Roofs: Case of Southern Italy," Sustainability, MDPI, vol. 12(23), pages 1-16, December.
    3. Raluca-Andreea Felseghi & Ioan Așchilean & Nicoleta Cobîrzan & Andrei Mircea Bolboacă & Maria Simona Raboaca, 2021. "Optimal Synergy between Photovoltaic Panels and Hydrogen Fuel Cells for Green Power Supply of a Green Building—A Case Study," Sustainability, MDPI, vol. 13(11), pages 1-20, June.
    4. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    5. Wongchai Anupong & Iskandar Muda & Sabah Auda AbdulAmeer & Ibrahim H. Al-Kharsan & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    6. Liton Chandra Voumik & Shohel Md. Nafi & Festus Victor Bekun & Murat Ismet Haseki, 2023. "Modeling Energy, Education, Trade, and Tourism-Induced Environmental Kuznets Curve (EKC) Hypothesis: Evidence from the Middle East," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    7. Aurelija Daugelaite & Indre Grazuleviciute-Vileniske, 2021. "The Relationship between Ethics and Aesthetics in Sustainable Architecture of the Baltic Sea Region," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
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