IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i12p5170-d1416885.html
   My bibliography  Save this article

Comprehensive Analysis of Influencing Factors on Building Energy Performance and Strategic Insights for Sustainable Development: A Systematic Literature Review

Author

Listed:
  • Razak Olu-Ajayi

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Hafiz Alaka

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Christian Egwim

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Ketty Grishikashvili

    (Big Data Technologies and Innovation Laboratory, University of Hertfordshire, Hatfield AL10 9AB, UK)

Abstract

A prerequisite for decreasing the intensification of energy in buildings is to evaluate and understand the influencing factors of building energy performance (BEP). These factors include building envelope features and outdoor climactic conditions, among others. Based on the importance of the influencing factors in the development of the building energy prediction model, various researchers are continuously employing different types of factors based on their popularity in academic literature, without a proper investigation of the most relevant factors, which, in some cases, potentially leads to poor model performance. However, this can be due to the absence of an adequate comprehensive analysis or review of all factors influencing BEP ubiquitously. Therefore, this paper conducts a holistic and comprehensive review of studies that have explored the various factors influencing energy use in residential and commercial buildings. In total, 74 research articles were systematically selected from the Scopus, ScienceDirect, and Institute of Electrical Electronics Engineers (IEEE) databases. Subsequently, by means of a systematic and bibliometric analysis, this paper comprehensively analyzed several important factors influencing BEP. The results reveals the important factors (such as windows and roofs) and engendered or shed light on the application of some energy-efficient strategies such as the utilization of a green roof and photovoltaic (PV) window, among others.

Suggested Citation

  • Razak Olu-Ajayi & Hafiz Alaka & Christian Egwim & Ketty Grishikashvili, 2024. "Comprehensive Analysis of Influencing Factors on Building Energy Performance and Strategic Insights for Sustainable Development: A Systematic Literature Review," Sustainability, MDPI, vol. 16(12), pages 1-27, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:5170-:d:1416885
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/12/5170/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/12/5170/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    2. Robinson, Caleb & Dilkina, Bistra & Hubbs, Jeffrey & Zhang, Wenwen & Guhathakurta, Subhrajit & Brown, Marilyn A. & Pendyala, Ram M., 2017. "Machine learning approaches for estimating commercial building energy consumption," Applied Energy, Elsevier, vol. 208(C), pages 889-904.
    3. Pang, Zhihong & O'Neill, Zheng, 2018. "Uncertainty quantification and sensitivity analysis of the domestic hot water usage in hotels," Applied Energy, Elsevier, vol. 232(C), pages 424-442.
    4. Ciulla, Giuseppina & Lo Brano, Valerio & D’Amico, Antonino, 2016. "Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level," Applied Energy, Elsevier, vol. 183(C), pages 1021-1034.
    5. Min, Jihoon & Azevedo, Inês Lima & Hakkarainen, Pekka, 2015. "Assessing regional differences in lighting heat replacement effects in residential buildings across the United States," Applied Energy, Elsevier, vol. 141(C), pages 12-18.
    6. Ihara, Takeshi & Gustavsen, Arild & Jelle, Bjørn Petter, 2015. "Effect of facade components on energy efficiency in office buildings," Applied Energy, Elsevier, vol. 158(C), pages 422-432.
    7. Huang, Yu & Niu, Jian-lei & Chung, Tse-ming, 2013. "Study on performance of energy-efficient retrofitting measures on commercial building external walls in cooling-dominant cities," Applied Energy, Elsevier, vol. 103(C), pages 97-108.
    8. Zhang, L.Y. & Jin, L.W. & Wang, Z.N. & Zhang, J.Y. & Liu, X. & Zhang, L.H., 2017. "Effects of wall configuration on building energy performance subject to different climatic zones of China," Applied Energy, Elsevier, vol. 185(P2), pages 1565-1573.
    9. Wang, Ruiqi & Zhao, Huan & Wu, Yan & Wang, Yufei & Feng, Xiao & Liu, Mengxi, 2018. "An industrial facility layout design method considering energy saving based on surplus rectangle fill algorithm," Energy, Elsevier, vol. 158(C), pages 1038-1051.
    10. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation," Energy, Elsevier, vol. 244(PB).
    11. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    12. Ki Uhn Ahn & Deuk Woo Kim & Young Jin Kim & Seong Hwan Yoon & Cheol Soo Park, 2016. "Issues to Be Solved for Energy Simulation of An Existing Office Building," Sustainability, MDPI, vol. 8(4), pages 1-12, April.
    13. Kristian Skeie & Arild Gustavsen, 2021. "Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling," Energies, MDPI, vol. 14(4), pages 1-32, February.
    14. Qiu, Changyu & Yi, Yun Kyu & Wang, Meng & Yang, Hongxing, 2020. "Coupling an artificial neuron network daylighting model and building energy simulation for vacuum photovoltaic glazing," Applied Energy, Elsevier, vol. 263(C).
    15. Florides, G. A. & Tassou, S. A. & Kalogirou, S. A. & Wrobel, L. C., 2002. "Measures used to lower building energy consumption and their cost effectiveness," Applied Energy, Elsevier, vol. 73(3-4), pages 299-328, November.
    16. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
    17. Wu, Wenbo & Dong, Bing & Wang, Qi (Ryan) & Kong, Meng & Yan, Da & An, Jingjing & Liu, Yapan, 2020. "A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption," Applied Energy, Elsevier, vol. 278(C).
    18. Li, Cheng & Hong, Tianzhen & Yan, Da, 2014. "An insight into actual energy use and its drivers in high-performance buildings," Applied Energy, Elsevier, vol. 131(C), pages 394-410.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    2. Leccese, Francesco & Salvadori, Giacomo & Asdrubali, Francesco & Gori, Paola, 2018. "Passive thermal behaviour of buildings: Performance of external multi-layered walls and influence of internal walls," Applied Energy, Elsevier, vol. 225(C), pages 1078-1089.
    3. Villa-Arrieta, Manuel & Sumper, Andreas, 2018. "A model for an economic evaluation of energy systems using TRNSYS," Applied Energy, Elsevier, vol. 215(C), pages 765-777.
    4. Liu, Che & Li, Fan & Zhang, Chenghui & Sun, Bo & Zhang, Guanguan, 2023. "A day-ahead prediction method for high-resolution electricity consumption in residential units," Energy, Elsevier, vol. 265(C).
    5. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
    6. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    7. Yuan, Jun & Nian, Victor & Su, Bin, 2019. "Evaluation of cost-effective building retrofit strategies through soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Chaudhary, Gaurav & New, Joshua & Sanyal, Jibonananda & Im, Piljae & O’Neill, Zheng & Garg, Vishal, 2016. "Evaluation of “Autotune” calibration against manual calibration of building energy models," Applied Energy, Elsevier, vol. 182(C), pages 115-134.
    9. Cao, Wenqiang & Yu, Junqi & Chao, Mengyao & Wang, Jingqi & Yang, Siyuan & Zhou, Meng & Wang, Meng, 2023. "Short-term energy consumption prediction method for educational buildings based on model integration," Energy, Elsevier, vol. 283(C).
    10. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    11. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    12. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    13. Jian Yao, 2014. "A Multi-Objective (Energy, Economic and Environmental Performance) Life Cycle Analysis for Better Building Design," Sustainability, MDPI, vol. 6(2), pages 1-13, January.
    14. Kim, Min-Hwi & Kim, Deukwon & Heo, Jaehyeok & Lee, Dong-Won, 2020. "Energy performance investigation of net plus energy town: Energy balance of the Jincheon Eco-Friendly energy town," Renewable Energy, Elsevier, vol. 147(P1), pages 1784-1800.
    15. Mao, Ning & Pan, Dongmei & Li, Zhao & Xu, Yingjie & Song, Mengjie & Deng, Shiming, 2017. "A numerical study on influences of building envelope heat gain on operating performances of a bed-based task/ambient air conditioning (TAC) system in energy saving and thermal comfort," Applied Energy, Elsevier, vol. 192(C), pages 213-221.
    16. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    17. Sadineni, Suresh B. & France, Todd M. & Boehm, Robert F., 2011. "Economic feasibility of energy efficiency measures in residential buildings," Renewable Energy, Elsevier, vol. 36(11), pages 2925-2931.
    18. Chandan Swaroop Meena & Binju P Raj & Lohit Saini & Nehul Agarwal & Aritra Ghosh, 2021. "Performance Optimization of Solar-Assisted Heat Pump System for Water Heating Applications," Energies, MDPI, vol. 14(12), pages 1-17, June.
    19. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    20. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:5170-:d:1416885. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.