Decision Support System for Sustainable Retrofitting of Existing Commercial Office Buildings
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With more than 60% of the inventory being over thirty years old, commercial office buildings represent a substantial global energy consumer. The Australian government has attempted to lower greenhouse gas emissions through legislation, but the implementation of these efforts has only resulted in annual reductions of 1-3%. It is essential to focus energy-efficient interventions on the stock of current commercial buildings if we are to achieve net zero emissions by 2050. Energy performance, efficiency, and greenhouse gas emissions can all be improved in commercial buildings by reducing energy consumption. According to Climate Works Australia and the IPCC, there is a 30% chance of avoiding current energy use while still reaping net economic benefits. To lessen global warming, the IPCC has also recommended that developed nations, like Australia, reduce emissions by 45% by 2030. Buildings with passive technologies can have better energy efficiency without sacrificing comfort. One of the main tactics for lowering energy consumption and carbon emissions in already-existing commercial buildings is energy retrofitting.
"Providing a machine with a part, or a place with equipment which was not originally present when it was built" is what the Cambridge Dictionary defines as "retrofitting." However, in this context, it refers to any intervention activity that involves modernizing or repurposing the current structure to satisfy an appropriate requirement. Both cases deal with increasing a building's level of sustainability and energy efficiency through renovations.
Multiple combinations of applicable energy consumption-reducing measures that can be applied to retrofit a building present a major challenge to decision-makers in energy retrofit. The evaluation of life cycle cost (LCC) and life cycle analysis (LCA) during retrofits present additional difficulties. LCC and LCA are not used in tandem; additionally, selecting the most appropriate retrofitting strategy or set of measures can occasionally be challenging due to the inclusion of unqualified sustainable technology in listings and selections.
The current study intends to address the problems by creating a strong decision support system (RDSS) that integrates sustainable criteria, or triple bottom line TBLs (environmental, social, and economic benefits), in the energy retrofit decision-making process. This will lessen the difficulties encountered in making decisions that will lead to successful building appraisals. The predetermined objectives are meant to lead to the goal.
- Because of various technological alternatives, it may be vital to have a comparison to simplify sustainable technologies (STs) tools using SWOT/multiple criteria in TBL aspects.
- Providing an assessment method to merge LCA & LCC to balance environmental and economic performances and determine the impact of the building life cycle on the energy retrofit decision process.
- Address the challenges decision-makers encounter in dealing with changes due to building markets and regulations since legislation and public expectation drive sustainable buildings.
- To develop and validate a holistic optimum strategic decision model to select the best retrofit alternatives for a particular building which maximizes the sustainability ranking of the building.
Initial research focuses on conducting a life-cycle cost analysis of a commercial office car park building in Sydney, New South Wales. The evaluation includes assessing energy performance through retrofit measures to determine long-term benefits. By using life-cycle cost analysis, the study aims to enhance decision-making in energy assessment.
To examine energy consumption intensity, lifecycle costing, CO2 emissions, and cost efficiency, data will be collected from non-green buildings and one building's envelope will be simulated using the Energy Plus tool. Experimental measurements will be compared to validate simulated models.
The study includes a case study on a 12,000 square meter commercial office building used as a commercial parking facility. Retrofitting activities were initiated on three office rooms, focusing on HVAC, lighting, and equipment improvements, resulting in a 1.9-year payback period, 15% emissions reduction, 25% energy savings, and 23% cost savings.
The subsequent phase involves utilizing various methods such as concept mapping, focus groups, interviews, Questionnaire surveys, and statistical analysis (SPSS) to develop a robust decision support system (RDSS) for sustainable energy retrofits.
The overall goal is to establish a systematic decision support system to aid decision-makers and policymakers in improving energy efficiency in commercial office buildings by implementing passive technologies. The system will also recommend strategies to enhance financial outcomes through smart building operations and management implementations.
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Zuo, J. & Zhao, Z.-Y. 2014, 'Green building research–status and future agenda: A review', Renewable and Sustainable Energy Reviews, vol. 30, pp. 271-81.
Zmeureanu, R., Fazio, P., DePani, S., & Calla, R. (1999). Development of an energy rating system for existing houses. Energy and Buildings, 29(2), 107-119.
Zavadskas, E. K., Kaklauskas, A., Raslanas, S., & Krutinis, M. (2004). Peculiarities of multi-criteria e-trade system application in the real estate sector. Journal of Civil Engineering and Management, 10, 71-78.
Zavadskas, E. K., & Kaklauskas, A. (1999). A new method of complex evaluation of projects based on multiple criteria analysis and the principle of proportionality. Integrating Technology & Human Decisions: Global Bridges into the 21st Century, 2(27), 2051-2053.
Yudelson, J. (2016). Reinventing green building: why certification systems aren't working and what we can do about it.
Woo, J., Wilsmann, J. & Kang, D. 2010, 'Use of as-built building information modeling', Construction Research Congress 2010: Innovation for Reshaping Construction Practice, pp. 538-48.
Wong, J.K.W. & Zhou, J. 2015, 'Enhancing environmental sustainability over building life cycles through green BIM: A review', Automation in Construction, vol. 57, pp. 156- 65.
Wilkinson, S. (2012). Analyzing sustainable retrofit potential in premium office buildings. Structural Survey, 30, pp. 398 - 410.
Wang, B., Xia, X. & Zhang, J. 2014, 'A multi-objective optimization model for the life- cycle cost analysis and retrofitting planning of buildings', Energy and Buildings, vol. 77, pp. 227-35.
United Nations Center for Human Settlements (2015) Global Report on Human Settlements. In Oxford University Press, New York.
Trotta, G. 2018, 'The determinants of energy efficient retrofit investments in the English residential sector', Energy Policy, vol. 120, pp. 175-82.
Torgal, F. P., Granqvist, C. G., Jelle, B. P., & Vanoli, G. P. (2017). The cost-effective energy efficient building is retrofitting materials, technologies, optimization, and case studies. Duxford, United Kingdom, Woodhead Publishing. http://public.eblib.com/choice/ publicfullrecord.aspx?p=4775703.
Standards Australia 2014, AS/NZS 3598.1:2014: Energy audits - Commercial buildings, Standards Australia & Standards New Zealand, Sydney.
Sioshansi, F. P., & Sioshansi, F. P. (2015). Energy, Sustainability, and Environment Technology, Incentives, Behavior. Burlington, Elsevier Science.
Shrestha, S. (2016). Comparison of energy efficient and green buildings technological and policy aspects with case studies from Europe, the USA, India, and Nepal.
Shipley, R., Utz, S., & Parsons, M. (2006). Does adaptive reuse pay? A study of the business of building renovation in Ontario, Canada. International Journal of Heritage Studies, 12(6), 505-520.
Sayigh, A. A. M. (2017). Sustainable High-Rise Buildings in Urban Zones: Advantages, Challenges, and Global Case Studies.
Saporito, A., Day, A. R., Karayiannis, T. G., & Parand, F. (2001). Multi-parameter building thermal analysis using the lattice method for global optimization. Energy and buildings, 33(3), 267-274.
Samandar, M.S. 2015, 'Construction Process Sustainability Index (CPSI): An Integrated Assessment Framework', North Carolina State University.
RUuckert, K., & Shahriari, E. (2014). Guideline for Sustainable, Energy Efficient Architecture & Construction. Berlin, Technische Uni Berlin.
Runeson, G. & de Valence, G. 2015, 'A critique of the methodology of building economics: trust the theories', Construction Management and Economics, vol. 33, no. 2, pp. 117-25.
Ries, C., Jenkins, J., & Wise, O. (2009). Improving the energy performance of buildings learning from the European Union and Australia. Santa Monica, CA, RAND.
Radziejowska, A. & Orłowski, Z. 2016, 'Method for assessing the social utility properties of a building', Procedia Engineering, vol. 161, pp. 765-70.
Preston, DJ & Woodbury, KA 2013, ‘Cost-benefit analysis of retrofit of high-intensity discharge factory lighting with energy-saving alternatives,' Energy Efficiency, vol. 6, no. 2, pp. 255-69.
Pomponi, F. & Moncaster, A. 2017, 'Circular economy for the built environment: A research framework', Journal of Cleaner Production, vol. 143, pp. 710-8.
Peng, C., Wang, L. & Zhang, X. 2014, 'DeST-based dynamic simulation and energy efficiency retrofit analysis of commercial buildings in the hot summer/cold winter zone of China: A case in Nanjing', Energy and Buildings, vol. 78, pp. 123-31.
Nik, V & Kalagasidis, 2013, ‘Impact study of the climate change on the energy performance of the building stock in Stockholm considering four climate uncertainties,' Building and Environment, vol. 60.
Mauro, G.M., Hamdy, M., Vanoli, G.P., Bianco, N. & Hensen, J.L. 2015, 'A new methodology for investigating the cost-optimality of energy retrofitting a building category', Energy and Buildings, vol. 107, pp. 456-78.
Liu, S., Meng, X. & Tam, C. 2015, 'Building information modeling-based building design optimization for sustainability', Energy and Buildings, vol. 105, pp. 139-53.
Langston, C., Wong, F.K., Hui, E.C. & Shen, L.-Y. 2008, 'Strategic assessment of building adaptive reuse opportunities in Hong Kong', Building and Environment, vol. 43, no. 10, pp. 1709-18.
Kubba, S. (2015). Handbook of green building design and construction: LEED, BREEAM, and Green Globes.
Kolokotsa, D., Diakaki, C., Grigoroudis, E., Stavrakakis, G. & Kalaitzakis, K. 2009, 'Decision support methodologies on the energy efficiency and energy management in buildings', Advances in Building Energy Research, vol. 3, no. 1, pp. 121-46.
Asadi, E., da Silva, M.G., Antunes, C.H., Dias, L. & Glicksman, L. 2014, 'Multi-objective optimization for building retrofit: A model using the genetic algorithm and artificial neural network and an application', Energy and Buildings, vol. 81, pp. 444-56.
ASHRAE, Energy Standard for Buildings Except for Low-Rise Residential Buildings, ANSI/ ASHRAE/IESNA Standard 90.1-2014. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA, 2004a.
Atici, K. B.; Ulucan, A. 2011. A multicriteria energy decision support system, Technological and Economic Development of Economy 17(2): 219–245.
Boardman, B. (2007), “Examining the carbon agenda via the 40% house scenario”, Building Research & Information, Vol. 35 No. 4, pp. 363-378
Bradley, B.A., Dhakal, R.P., Cubrinovski, M. and MacRae, G.A. (2008), "Seismic loss estimation for efficient decision making," New Zealand Society for Earthquake Engineering (NZSEE) Conference, Engineering an Earthquake.
Brito, A. J.; Almeida, A. T. 2008. Multi-attribute risk assessment for risk ranking of natural gas pipelines, Reliability Engineering & System Safety 14: 69–82
Cappers, P.; Goldman, Ch. 2010. The financial impact of energy efficiency under a federal combined efficiency and renewable electricity standard: a Case study of a Kansas “super-utility”, Energy Policy 38(8): 3998–401.
Chidiac, S., Catania, E., Morofsky, E. & Foo, S. 2011, 'Effectiveness of single and multiple energy retrofit measures on the energy consumption of office buildings', Energy, vol. 36, no. 8, pp. 5037-52.
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.
Chen, X. (2023). Real-Time Detection of Adversarial Attacks in Deep Learning Models. MZ Computing Journal, 4(2).
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.
Chen, X. (2023). Efficient Algorithms for Real-Time Semantic Segmantation in Augmented reality. Innovative Computer Sciences Journal, 9(1).
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.
Chen, X. (2023). Optimization Strategies for Reducing Energy Consumption in AI Model Training. Advances in Computer Sciences, 6(1).
. 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.
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.
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).
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.
JALA, S., ADHIA, N., KOTHARI, M., JOSHI, D., & PAL, R. SUPPLY CHAIN DEMAND FORECASTING USING APPLIED MACHINE LEARNING AND FEATURE ENGINEERING.
Lin, Z., Wang, Z., Zhu, Y., Li, Z., & Qin, H. (2024). Text Sentiment Detection and Classification Based on Integrated Learning Algorithm. Applied Science and Engineering Journal for Advanced Research, 3(3), 27-33.
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.
54. 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.
Esfahani, M. N. (2024). Content Analysis of Textbooks via Natural Language Processing. American Journal of Education and Practice, 8(4), 36-54.
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).
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