Multi-Objective Optimization Of Sustainable Building Designs For Energy Consumption Using Ai -Ml Framework
DOI:
https://doi.org/10.61841/5x02xp21Keywords:
Multi-Objective, Optimization, Frameworks, Construction, Sustainable Buildings, AI-ML Techniques.Abstract
Sustainable building design plays a crucial role in reducing energy consumption and promoting environmental conservation. In this context, multi-objective optimization techniques combined with artificial intelligence (AI) and machine learning (ML) frameworks have emerged as powerful tools to enhance energy efficiency in buildings. This paper presents a comprehensive study on the application of AI-ML frameworks for multi-objective optimization of sustainable building designs, specifically focusing on energy consumption.
The proposed framework leverages the capabilities of AI and ML algorithms to generate optimal solutions by simultaneously considering multiple conflicting objectives, such as minimizing energy consumption, maximizing occupant comfort, and reducing greenhouse gas emissions. The framework integrates various components, including data acquisition, pre-processing, feature extraction, model training, optimization algorithms, and performance evaluation. Through the application of AI-ML techniques, the framework utilizes historical building energy consumption data, weather patterns, building characteristics, and occupant behaviour to train predictive models. These models are then employed to simulate and evaluate different design scenarios, generating a set of Pareto-optimal solutions. The Pareto front represents the trade-offs between energy efficiency and other design criteria, enabling decision-makers to select the most appropriate sustainable building designs. The advantages of the AI-ML framework for multi-objective optimization of sustainable building designs are manifold. It enables rapid exploration of a wide range of design alternatives, improving decisionmaking efficiency and flexibility. Moreover, it facilitates the incorporation of dynamic factors such as weather patterns and occupant behaviour, enhancing the accuracy and adaptability of the optimization process. To validate the effectiveness of the proposed framework, several case studies are conducted using real-world building data. The results demonstrate that the AI-ML framework outperforms traditional optimization methods in terms of energy efficiency, occupant comfort, and environmental impact. Furthermore, sensitivity analyses are performed to investigate the robustness and generalizability of the framework under different scenarios.
The integration of AI and ML techniques in multi-objective optimization frameworks for sustainable building design provides a powerful approach to improve energy efficiency and promote environmentally conscious construction practices. This research contributes to the advancement of intelligent building design processes, enabling stakeholders to make informed decisions that balance energy consumption, occupant comfort, and environmental sustainability. The findings of this study have significant implications for architects, engineers, policymakers, and researchers involved in the design and construction of sustainable buildings.
Downloads
References
[1]. International Energy Agency (IEA). Energy Technology Perspectives 2012: Pathways to a Clean Energy System;
International Energy Agency: Paris, France, 2012.
[2]. De Wilde, P. The Gap between predicted and measured energy performance of buildings: A framework for
investigation. Automat. Constr. 2014, 41, 40–49
[3]. Ahn, K.U.; Kim, D.W.; Kim, Y.J.; Yoon, S.H.; Park, C.S. Issues to be solved for energy simulation of an existing
office building. Sustainability 2016, 8, 345.
[4]. Ramesh, T.; Prakash, R.; Shukla, K.K. Life cycle energy analysis of buildings: An overview. Energy Build. 2010, 42,
1592–1600.
[5]. Ghiassi, N. An Hourglass Approach to Urban Energy Computing. Ph.D. Thesis, Vienna University of Technology,
Vienna, Austria, 2017
[6]. Salom, J.; Pascual, J. Residential Retrofits at District Scale; InnoEnergy: Eindhoven, The Netherlands, 2017
[7]. Reinhart, C.F.; Cerezo Davila, C. Urban building energy modeling—A review of a nascent field. Build.
Environ. 2016, 97, 196–202
[8]. Carreras, J.; Boer, D.; Guillén-Gosálbez, G.; Cabeza, L.F.; Medrano, M.; Jiménez, L. Multi-objective optimization
of thermal modelled cubicles considering the total cost and life cycle environmental impact. Energy Build. 2015, 88,
335–346.
[9]. Hamdy, M.; Hasan, A.; Siren, K. A multi-stage optimization method for cost-optimal and nearly-zero-energy
building solutions in line with the EPBD-recast 2010. Energy Build. 2013, 56, 189–203
[10]. Penna, P.; Prada, A.; Cappelletti, F.; Gasparella, A. Multi-objectives optimization of energy efficiency measures in
existing buildings. Energy Build. 2015, 95, 57–69.
[11]. Shao, Y.; Geyer, P.; Lang, W. Integrating requirement analysis and multi-objective optimization for office building
energy retrofit strategies. Energy Build. 2014, 82, 356–368.
[12]. Wang, C.; Kilkis, S.; Tjernström, J.; Nyblom, J.; Martinac, I. Multi-objective optimization and parametric analysis
of energy system designs for the Albano University Campus in Stockholm. Procedia Eng. 2017, 180, 621–630.
[13]. Harish, V.S.K.V.; Kumar, A. A review on modeling and simulation of building energy systems. Renew. Sustain.
Energy Rev. 2016, 56, 1272–1292.
[14]. Dorer, V.; Allegrini, J.; Orehounig, K.; Moonen, P.; Upadhyay, G.; Kämpf, J.H.; Carmeliet, J. Modelling the urban
microclimate and its impact on the energy demand of buildings and building clusters. In Proceedings of the Building
Simulation 2013, Chambery, France, 26–28 August 2013; Wurtz, E., Ed.; IBPSA: Toronto, ON, Canada, 2013.
[15]. Gros, A.; Bozonnet, E.; Inard, C.; Musy, M. Simulation tools to assess microclimate and building energy—A case
study on the design of a new district. Energy Build. 2016, 114, 112–122
[16]. Keirstead, J.; Jennings, M.; Sivakumar, A. A review of urban energy system models: Approaches, challenges and
opportunities. Renew. Sustain. Energy Rev. 2012, 16, 3847–3866
[17]. Kavgic, M.; Mavrogianni, A.; Mumovic, D.; Summerfield, A.J.; Stevanović, Z.M.; Djurović-Petrović, M.D. A
review of bottom-up building stock models for energy consumption in the residential sector. Build.
Environ. 2010, 45, 1683–1697.
[18]. Perez, D.; Robinson, D. Urban energy flow modelling: A data-aware approach. In Digital Urban Modeling and
Simulation. Communications in Computer and Information Science; Müller Arisona, S., Aschwanden, G., Halatsch,
J., Wonka, P., Eds.; Springer: Berlin, Germany, 2012; Volume 242, pp. 200–220. [Google Scholar]
[19]. Swan, L.G.; Ugursal, V.I. Modeling of end-use energy consumption in the residential sector: A review of modeling
techniques. Renew. Sustain. Energy Rev. 2009, 13, 1819–1835.
[20]. Bueno, B.; Pigeon, G.; Norford, L.K.; Zibouche, K.; Marchadier, C. Development and evaluation of a building
energy model integrated in the TEB scheme. Geosci. Model Dev. 2012, 5, 433–448.
[21]. Swan, L.G.; Ugursal, V.I.; Beausoleil-Morrison, I. Implementation of a Canadian residential energy end-use model
for assessing new technology impacts. In Proceedings of the Building Simulation 2009, Glasgow, UK, 27–30 July
2009. [Google Scholar]
[22]. Chen, Y.; Hong, T.; Piette, M.A. Automatic generation and simulation of urban building energy models based on
city datasets for city-scale building retrofit analysis. Appl. Energy 2017, 205, 323–335.
[23]. Nouvel, R.; Brassel, K.H.; Bruse, M.; Duminil, E.; Coors, V.; Eicker, U.; Robinson, D. SimStadt, a new workflowdriven urban energy simulation platform for CityGML city models. In Proceedings of the International Conference
CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale, EPFL, Lausanne,
Switzerland, 9–15 September 2015; Scartezzini, J.-L., Ed.; EPFL Solar Energy and Building Physics Laboratory:
Lausanne, Switzerland, 2015.
[24]. Remmen, P.; Müller, D.; Lauster, M.; Osterhage, T.; Mans, M. CityGML import and export for dynamic building
performance simulation in Modelica. In Proceedings of the Building Simulation and Optimization 2016, Newcastle
University, Newcastle upon Tyne, UK, 12–14 September 2016.
[25]. Reinhart, C.F.; Dogan, T.; Jakubiec, J.A.; Rakha, T.; Sang, A. Umi-an Urban Simulation Environment for Building
Energy Use, Daylighting and Walkability. In Proceedings of the Building Simulation 2013, Chambery, France, 26–
28 August 2013; Wurtz, E., Ed.; IBPSA: Toronto, ON, Canada, 2013.
[26]. Best, R.E.; Flager, F.; Lepech, M.D. Modeling and optimization of building mix and energy supply technology for
urban districts. Appl. Energy 2015, 159, 161–177.
[27]. Fonseca, J.A.; Nguyen, T.-A.; Schlueter, A.; Marechal, F. City Energy Analyst (CEA): Integrated framework for
analysis and optimization of building energy systems in neighborhoods and city districts. Energy Build. 2016, 113,
202–226.
[28]. Robinson, D.; Haldi, F.; Leroux, P.; Perez, D.; Rasheed, A.; Wilke, U. CitySim: Comprehensive micro-simulation
of resource flows for sustainable urban planning. In Proceedings of the Building Simulation 2009, Glasgow, UK,
27–30 July 2009.
[29]. Bollinger, L.A.; Evins, R. HUES: A holistic urban energy simulation platform for effective model integration. In
Proceedings of the International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano
to Urban Scale, EPFL, Lausanne, Switzerland, 9–15 September 2015; Scartezzini, J.-L., Ed.; EPFL Solar Energy
and Building Physics Laboratory: Lausanne, Switzerland, 2015.
[30]. Baetens, R.; De Coninck, R.; Van Roy, J.; Verbruggen, B.; Driesen, J.L.J.; Helsen, L.M.L.; Saelens, D. Assessing
electrical bottlenecks at feeder level for residential net zero-energy buildings by integrated system simulation. Appl.
Energy 2012, 96, 74–83
[31]. Bergerson, J.; Muehleisen, R.T.; Bo Rodda, W.; Auld, J.A.; Guzowski, L.B.; Ozik, J.; Collier, N. Designing Future
Cities: LakeSIM Integrated Design Tool for Assessing Short and Long Term Impacts of Urban Scale Conceptual
Designs; ISOCARP Review 11; Nan, S., Reilly, J., Klass, F., Eds.; International Society of City and Regional
Planners: Sura Mica, Romania, 2015.
[32]. Molitor, C.; Groß, S.; Zeitz, J.; Monti, A. MESCOS—A multienergy system cosimulator for city district energy
systems. IEEE Trans. Ind. Inform. 2014, 10, 2247–2256.
[33]. Keirstead, J.; Samsatli, N.; Shah, N. SynCity: An integrated tool kit for urban energy systems modelling. In
Proceedings of the 5th Urban Research Symposium, Marseille, France, 28–30 June 2009.
[34]. Polly, B.; Kutscher, C.; Macumber, D.; Schott, M.; Pless, S.; Livingood, B.; Van Geet, O. From zero energy buildings
to zero energy districts. In Proceedings of the 2016 ACEEE Summer Study on Energy Efficiency in Buildings: From
Components to Systems, From Buildings to Communities, Pacific Grove, CA, USA, 21–26 August 2016.
[35]. Wetter, M.; Haves, P. A modular building controls virtual test bed for the integration of heterogeneous systems. In
Proceedings of the SimBuild 2008, Berkeley, SF, USA, 30 July–1 August 2008.
[36]. Abbasabadi, N.; Ashayeri, M. Urban energy use modeling methods and tools: A review and an outlook. Build.
Environ. 2017, 161, 106270.
[37]. Cerezo, C.; Dogan, T.; Reinhart, C.F. Towards standardized building properties template files for early design energy
model generation. In Proceedings of the 2014 ASHRAE/IBPSA-USA Building Simulation Conference, Atlanta, GA,
USA, 10–12 September 2014.
[38]. Kämpf, J.H.; Robinson, D. A simplified thermal model to support analysis of urban resource flows. Energy
Build. 2007, 39, 445–453.
[39]. Zekar, A.; El Khatib, S. Development and assessment of simplified building representations under the context of an
urban energy model: Application to arid climate environment. Energy Build. 2017, 173, 461–469.
[40]. Biljecki, F.; Stoter, J.; Ledoux, H.; Zlatanova, S.; Coltekin, A. Applications of 3D city models: State of the art
review. ISPRS Int. J. Geo-Inf. 2015, 4, 2842–2889.
[41]. Karmellos, M.; Mavrotas, G. Multi-objective optimization and comparison framework for the design of distributed
energy systems. Energy Convers. Manag. 2017, 180, 473–495.
[42]. Maroufmashat, A.; Sattari, S.; Roshandel, R.; Fowler, M.; Elkamel, A. Multi-objective optimization for design and
operation of distributed energy systems through the multi-energy hub network approach. Ind. Eng. Chem.
Res. 2016, 55, 8950–8966.
[43]. Falke, T.; Krengel, S.; Meinerzhagen, A.-K.; Schnettler, A. Multi-objective optimization and simulation model for
the design of distributed energy systems. Appl. Energy 2016, 184, 1508–1516.
[44]. Camporeale, P.E.; Mercader-Moyano, P. Towards nearly zero energy buildings: Shape optimization of typical
housing typologies in Ibero-American temperate climate cities from a holistic perspective. Sol. Energy 2016, 193,
738–765.
[45]. Waibel, C.; Evins, R.; Carmeliet, J. Co-Simulation and optimization of building geometry and multi-energy systems:
Interdependencies in energy supply, energy demand and solar potentials. Appl. Energy 2017, 242, 1661–1682.
[46]. Vermeulen, T.; Knopf-Lenoir, C.; Villon, P.; Beckers, B. Urban layout optimization framework to maximize direct
solar irradiation. Comput. Environ. Urban Syst. 2015, 51, 1–12.
[47]. Yu, S.; Austern, G.; Jirathiyut, T.; Moral, M. Climatic formations: Evolutionary dynamics of urban morphologies. J.
Asian Arch. Build. Eng. 2014, 13, 317–324.
[48]. Luddeni, G.; Krarti, M.; Pernigotto, G.; Gasparella, A. An analysis methodology for large-scale deep energy retrofits
of existing building stocks: Case study of the Italian office building. Sustain. Cities Soc. 2017, 41, 296–311. [Google
Scholar] [CrossRef]
[49]. Chen, Y.; Hong, T.; Luo, X.; Hooper, B. Development of city buildings dataset for urban building energy
modeling. Energy Build. 2016, 183, 252–265.
[50]. Prando, D.; Prada, A.; Ochs, F.; Gasparella, A.; Baratieri, M. Analysis of the energy and economic impact of costoptimal buildings refurbishment on district heating systems. Sci. Technol. Built Environ. 2015, 21, 876–891
[51]. Ente Nazionale Italiano di Normazione (UNI). UNI/TS 11300-1—Energy Performance of Buildings Part 1:
Evaluation of Energy need for Space Heating and Cooling; UNI: Milan, Italy, 2014.
[52]. Kämpf, J.H.; Emmanuel, W. A verification of CitySim results using the BESTEST and monitored consumption
values. In Proceedings of the Building Simulation Applications BSA 2015, Bolzano, Italy, 4–6 February 2015;
Baratieri, M., Corrado, V., Gasparella, A., Patuzzi, F., Eds.; Bozen-Bolzano University Press: Bolzano, Italy, 2015.
[53]. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2007: Synthesis Report. Contribution of
Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change;
Core Writing Team, Pachauri, R.K., Reisinger, A., Eds.; IPCC: Geneva, Switzerland, 2008.
[54]. Organisation for Economic Co-operation and Development (OCED). The Economics of Climate Change Mitigation:
Policies and Options for Global Action beyond 2012; OECD: Paris, France, 2012.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Author
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
