ISSN (Online): 2321-3418
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Advanced Decision- Making Framework for Sustainable Energy Retrofit of Existing Commercial Office Buildings

DOI: 10.18535/ijsrm/v12i08.em26· Pages: 7269-7297· Vol. 12, No. 08, (2024)· Published: August 27, 2024
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Abstract

In the background of rising global initiatives to combat climate change and promote better energy management, retrofitting of current structures has become a major strategy. This abstract and literature review aims at presenting a full review of a decision-making approach that can be used in choosing sustainable options for energy retrofitting. This particular framework is designed to help guide retrofit decision-making due to the many issues that surround the process by providing an economic as well as an environmental and social context. Energy retrofitting means improving the existing structure or complex to decrease its energy intensity and have a negative impact on the environment. As stated earlier, it is a critical process for attaining sustainability goals and advancing the performance of buildings. But the decision on which retrofit measures should be implemented can be rather difficult because of the availability of a vast number of technologies and because more factors must be considered, such as cost and energy savings.

The decision-making framework provided in this article aims at categorizing retrofit selection into this list of features and views the current article as a way of making the process more efficient. It encompasses the life-cycle cost analysis (LCCA), the economic valuation, and the multi-criteria decision analysis tools and criteria. These tools facilitate the evaluation of financial viability, the advantages of climatically retrofitting, and the environmental effects of distinct retrofitting choices. The above-mentioned framework is exemplified with a case study of an office building retrofit project. The following case includes an example of the application of the mentioned framework and illustrates the explanation of how it helps to make decisions. Due to the consideration of costs, energy, and environmental performance, the framework enables the stakeholders to choose appropriate retrofit measures.

Other than the case study, the framework also focuses on the need to establish a sustainable energy retrofit DSS. Thus, the DSS supplements collected data on building performance, retrofit technologies, and economic aspects, which can be accessed by the stakeholders in real-time and with built-in facilities for scenario analysis. This system improves decision-making since retrofit results can be monitored and evaluated frequently. Both tools and strategies for the decision-making of sustainable energy retrofits bring a logical and systematic approach aimed at neutralizing the decision-making challenges related to retrofit selection. By applying economic, environmental, and social factors in the decision-making process, the framework assists the stakeholders in reaching energy efficiency and sustainability goals. The development of another strong decision-support system also improves the working of the framework and checks that retrofit projects are done efficiently and are strategic to the general goal of sustainable development.

Keywords

Decision-Support System (DSS)Energy RetrofitsSustainabilityEnergy EfficiencyCostEffectivenessand Data Analysis Life-Cycle Cost Analysis (LCCA)Multi-Criteria Decision Analysis

References

  1. Ongpeng, J. M. C., Rabe, B. I. B., Razon, L. F., Aviso, K. B., & Tan, R. R. (2022). A multi-criterion decision analysis framework for sustainable energy retrofit in buildings. Energy, 239, 122315.Google Scholar ↗
  2. Stanica, D. I., Karasu, A., Brandt, D., Kriegel, M., Brandt, S., & Steffan, C. (2021). A methodology to support the decision-making process for energy retrofitting at district scale. Energy and Buildings, 238, 110842.Google Scholar ↗
  3. Jafari, A., & Valentin, V. (2017). An optimization framework for building energy retrofits decision-making. Building and environment, 115, 118-129.Google Scholar ↗
  4. Joshi, D., Parikh, A., Mangla, R., Sayed, F., & Karamchandani, S. H. (2021). AI Based Nose for Trace of Churn in Assessment of Captive Customers. Turkish Online Journal of Qualitative Inquiry, 12(6).Google Scholar ↗
  5. Lu, Y., Li, P., Lee, Y. P., & Song, X. (2021). An integrated decision-making framework for existing building retrofits based on energy simulation and cost-benefit analysis. Journal of Building Engineering, 43, 103200.Google Scholar ↗
  6. Egiluz, Z., Cuadrado, J., Kortazar, A., & Marcos, I. (2021). Multi-criteria decision-making method for sustainable energy-saving retrofit façade solutions. Sustainability, 13(23), 13168.Google Scholar ↗
  7. Joshi, D., Sayed, F., Saraf, A., Sutaria, A., & Karamchandani, S. (2021). Elements of Nature Optimized into Smart Energy Grids using Machine Learning. Design Engineering, 1886-1892.Google Scholar ↗
  8. Shen, P. (2024). Building energy retrofit optimization considering future climate and decision-making under various mindsets. Journal of Building Engineering, 110422.Google Scholar ↗
  9. Si, J. (2017). Green retrofit of existing non-domestic buildings as a multi criteria decision making process (Doctoral dissertation, UCL (University College London)).Google Scholar ↗
  10. JALA, S., ADHIA, N., KOTHARI, M., JOSHI, D., & PAL, R. SUPPLY CHAIN DEMAND FORECASTING USING APPLIED MACHINE LEARNING AND FEATURE ENGINEERING.Google Scholar ↗
  11. Jafari, A., & Valentin, V. (2018). Selection of optimization objectives for decision-making in building energy retrofits. Building and Environment, 130, 94-103.Google Scholar ↗
  12. Zheng, D. L., Yu, L. J., & Wang, L. Z. (2019). Decision-making method for building energy efficiency retrofit measures based on an improved analytic hierarchy process. Journal of Renewable and Sustainable Energy, 11(4).Google Scholar ↗
  13. Joshi, D., Sayed, F., Jain, H., Beri, J., Bandi, Y., & Karamchandani, S. A Cloud Native Machine Learning based Approach for Detection and Impact of Cyclone and Hurricanes on Coastal Areas of Pacific and Atlantic Ocean.Google Scholar ↗
  14. Dirutigliano, D., Delmastro, C., & Moghadam, S. T. (2018). A multi-criteria application to select energy retrofit measures at the building and district scale. Thermal Science and Engineering Progress, 6, 457-464.Google Scholar ↗
  15. Daniel, S., & Ghiaus, C. (2023). Multi-Criteria Decision Analysis for Energy Retrofit of Residential Buildings: Methodology and Feedback from Real Application. Energies, 16(2), 902.Google Scholar ↗
  16. Gultekin, P., J. Anumba, C., & M. Leicht, R. (2014). Case study of integrated decision-making for deep energy-efficient retrofits. International Journal of Energy Sector Management, 8(4), 434-455.Google Scholar ↗
  17. Liu, T., Ma, G., & Wang, D. (2022). Pathways to successful building green retrofit projects: Causality analysis of factors affecting decision making. Energy and Buildings, 276, 112486.Google Scholar ↗
  18. Carpino, C., Bruno, R., Carpino, V., & Arcuri, N. (2022). Improve decision-making process and reduce risks in the energy retrofit of existing buildings through uncertainty and sensitivity analysis. Energy for Sustainable Development, 68, 289-307.Google Scholar ↗
  19. Khambaty, A., Joshi, D., Sayed, F., Pinto, K., & Karamchandani, S. (2022, January). Delve into the Realms with 3D Forms: Visualization System Aid Design in an IOT-Driven World. In Proceedings of International Conference on Wireless Communication: ICWiCom 2021 (pp. 335-343). Singapore: Springer Nature Singapore.Google Scholar ↗
  20. Rahmouni, S., & Si Mohammed, A. (2024). An effective decision-making method for building retrofit measures strategy. Indoor and Built Environment, 1420326X241234817.Google Scholar ↗
  21. Balasbaneh, A. T., Yeoh, D., Ramli, M. Z., & Valdi, M. H. T. (2022). Different alternative retrofit to improving the sustainability of building in tropical climate: multi-criteria decision-making. Environmental Science and Pollution Research, 29(27), 41669-41683.Google Scholar ↗
  22. Dell’Anna, F. (2023). An ELECTRE TRI B-Based Decision Framework to Support the Energy Project Manager in Dealing with Retrofit Processes at District Scale. Sustainability, 15(2), 1250.Google Scholar ↗
  23. Si, J., & Marjanovic-Halburd, L. (2018). Criteria weighting for green technology selection as part of retrofit decision making process for existing non-domestic buildings. Sustainable Cities and Society, 41, 625-638.Google Scholar ↗
  24. Albatici, R., Gadotti, A., Baldessari, C., & Chiogna, M. (2016). A decision making tool for a comprehensive evaluation of building retrofitting actions at the regional scale. Sustainability, 8(10), 990.Google Scholar ↗
  25. JOSHI, D., SAYED, F., BERI, J., & PAL, R. (2021). An efficient supervised machine learning model approach for forecasting of renewable energy to tackle climate change. Int J Comp Sci Eng Inform Technol Res, 11, 25-32.Google Scholar ↗
  26. Sibilla, M., & Kurul, E. (2020). Transdisciplinarity in energy retrofit. A Conceptual Framework. Journal of cleaner production, 250, 119461.Google Scholar ↗
  27. Lombardi, P., Abastante, F., Torabi Moghadam, S., & Toniolo, J. (2017). Multicriteria spatial decision support systems for future urban energy retrofitting scenarios. Sustainability, 9(7), 1252.Google Scholar ↗
  28. Seddiki, M., Bennadji, A., Laing, R., Gray, D., & Alabid, J. M. (2021). Review of Existing Energy Retrofit Decision Tools for Homeowners. Sustainability, 13(18), 10189.Google Scholar ↗
  29. Caterino, N., Nuzzo, I., Ianniello, A., Varchetta, G., & Cosenza, E. (2021). A BIM-based decision-making framework for optimal seismic retrofit of existing buildings. Engineering Structures, 242, 112544.Google Scholar ↗
  30. Khambati, A. (2021). Innovative Smart Water Management System Using Artificial Intelligence. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 4726-4734.Google Scholar ↗
  31. Lizana, J., Molina-Huelva, M., & Chacartegui, R. (2016). Multi-criteria assessment for the effective decision management in residential energy retrofitting. Energy and Buildings, 129, 284-307.Google Scholar ↗
  32. Engelbrecht Foldager, H., Camillus Jeppesen, R., & Jradi, M. (2019). DanRETRO: A decision-making tool for energy retrofit design and assessment of Danish buildings. Sustainability, 11(14), 3794.Google Scholar ↗
  33. Arefin, S., & Kipkoech, G. (2024). Using AI and Precision Nutrition to Support Brain Health during Aging. Advances in Aging Research, 13(5), 85-106.Google Scholar ↗
  34. Menassa, C. C., & Baer, B. (2014). A framework to assess the role of stakeholders in sustainable building retrofit decisions. Sustainable Cities and Society, 10, 207-221.Google Scholar ↗
  35. Zhang, H., Feng, H., Hewage, K., & Arashpour, M. (2022). Artificial neural network for predicting building energy performance: a surrogate energy retrofits decision support framework. Buildings, 12(6), 829.Google Scholar ↗
  36. Gabrielli, L., & Ruggeri, A. G. (2019). Developing a model for energy retrofit in large building portfolios: Energy assessment, optimization and uncertainty. Energy and buildings, 202, 109356.Google Scholar ↗
  37. Kaashi, S., & Vilventhan, A. (2023). Development of a building information modelling based decision-making framework for green retrofitting of existing buildings. Journal of Building Engineering, 80, 108128.Google Scholar ↗
  38. Arefin, S. (2024). IDMap: Leveraging AI and Data Technologies for Early Cancer Detection. Valley International Journal Digital Library, 1138-1145.Google Scholar ↗
  39. Ruggeri, A. G., Gabrielli, L., & Scarpa, M. (2020). Energy retrofit in European building portfolios: A review of five key aspects. Sustainability, 12(18), 7465.Google Scholar ↗
  40. Chen, X. (2023). Real-Time Detection of Adversarial Attacks in Deep Learning Models. MZ Computing Journal, 4(2).Google Scholar ↗
  41. Chen, X. (2023). Efficient Algorithms for Real-Time Semantic Segmantation in Augmented reality. Innovative Computer Sciences Journal, 9(1).Google Scholar ↗
  42. Chen, X. (2023). Optimization Strategies for Reducing Energy Consumption in AI Model Training. Advances in Computer Sciences, 6(1).Google Scholar ↗
  43. Wang, Z., Zhu, Y., Li, Z., Wang, Z., Qin, H., & Liu, X. (2024). Graph neural network recommendation system for football formation. Applied Science and Biotechnology Journal for Advanced Research, 3(3), 33-39.Google Scholar ↗
  44. Wang, Z., Zhu, Y., He, S., Yan, H., & Zhu, Z. (2024). LLM for Sentiment Analysis in E-Commerce: A Deep Dive into Customer Feedback. Applied Science and Engineering Journal for Advanced Research, 3(4), 8-13.Google Scholar ↗
  45. Lyu, H., Wang, Z., & Babakhani, A. (2020). A UHF/UWB hybrid RFID tag with a 51-m energy-harvesting sensitivity for remote vital-sign monitoring. IEEE transactions on microwave theory and techniques, 68(11), 4886-4895.Google Scholar ↗
  46. Zhu, Z., Wang, Z., Wu, Z., Zhang, Y., & Bo, S. (2024). Adversarial for Sequential Recommendation Walking in the Multi-Latent Space. Applied Science and Biotechnology Journal for Advanced Research, 3(4), 1-9.Google Scholar ↗
  47. Qihong, Z., Guangzong, W., Zeyu, W., & Huihui, L. (2018, July). Development of Horizontal Stair-Climbing Platform for Smart Wheelchairs. In Proceedings of the 12th International Convention on Rehabilitation Engineering and Assistive Technology (pp. 57-60).Google Scholar ↗
Author details
Bengold Anarene
School of Built Environment Western Sydney University
✉ Corresponding Author
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