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Smart Energy Solutions as an Indispensable Multi-Criteria Input for a Coherent Urban Planning and Building Design Process—Two Case Studies for Smart Office Buildings in Warsaw Downtown Area

Author

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  • Elzbieta Rynska

    (Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, Poland)

  • Joanna Klimowicz

    (Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, Poland)

  • Slawomir Kowal

    (Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, Poland)

  • Krzysztof Lyzwa

    (Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, Poland)

  • Michal Pierzchalski

    (Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, Poland)

  • Wojciech Rekosz

    (Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, Poland)

Abstract

The introduction of parametric tools has made a strong shift within a traditional approach to urban planning and building design, including the creation of a design awareness zone where environmental issues are concerned. This approach also uses sufficient data to be used already at the concept stage and provides initial interdisciplinary solutions. Analyses from the very initial stages allow the inclusion of smart energy choices influencing the massing, architectural features, proportions, flexibility of design, and economics. This is only a threshold; there is still a place for further development and more accurate analyses leading to the construction of buildings and urban areas with a stronger input of sustainable solutions, as existing approaches have certain limitations. This path has been followed in several research grants conducted at the Faculty of Architecture Warsaw University of Technology, and later on developed as a co-operation area with various stakeholders. Outside the general state of art, this paper will include two case studies which were provided as a concept design for prospective investors. Both locations are in the Warsaw Downtown Area, and analyses include algorithmic models dealing with the optimisation of the buildings’ forms, urban scale sun radiation levels, shadow and wind analyses indicating use of sunlight energy and wind as alternative energy sources. One of the case studies contains Pareto Front including both single- and multi-criteria optimization methods for analysing energy and economic efficiency issues, pointing out the best case solutions.

Suggested Citation

  • Elzbieta Rynska & Joanna Klimowicz & Slawomir Kowal & Krzysztof Lyzwa & Michal Pierzchalski & Wojciech Rekosz, 2020. "Smart Energy Solutions as an Indispensable Multi-Criteria Input for a Coherent Urban Planning and Building Design Process—Two Case Studies for Smart Office Buildings in Warsaw Downtown Area," Energies, MDPI, vol. 13(15), pages 1-24, July.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3757-:d:387773
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    References listed on IDEAS

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    1. Xiaoyu Ying & Yanling Wang & Wenzhe Li & Ziqiao Liu & Grace Ding, 2020. "Group Layout Pattern and Outdoor Wind Environment of Enclosed Office Buildings in Hangzhou," Energies, MDPI, vol. 13(2), pages 1-16, January.
    2. Angelamaria Massimo & Marco Dell'Isola & Andrea Frattolillo & Giorgio Ficco, 2014. "Development of a Geographical Information System (GIS) for the Integration of Solar Energy in the Energy Planning of a Wide Area," Sustainability, MDPI, vol. 6(9), pages 1-15, August.
    3. Ali Sadollah & Mohammad Nasir & Zong Woo Geem, 2020. "Sustainability and Optimization: From Conceptual Fundamentals to Applications," Sustainability, MDPI, vol. 12(5), pages 1-34, March.
    4. S. Ruzika & M. M. Wiecek, 2005. "Approximation Methods in Multiobjective Programming," Journal of Optimization Theory and Applications, Springer, vol. 126(3), pages 473-501, September.
    5. Allegrini, Jonas & Orehounig, Kristina & Mavromatidis, Georgios & Ruesch, Florian & Dorer, Viktor & Evins, Ralph, 2015. "A review of modelling approaches and tools for the simulation of district-scale energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1391-1404.
    6. Mary Ann Curran, 2009. "Wrapping Our Brains around Sustainability," Sustainability, MDPI, vol. 1(1), pages 1-9, March.
    7. Chih-Hong Huang & Hsin-Hua Tsai & Hung-chen Chen, 2020. "Influence of Weather Factors on Thermal Comfort in Subtropical Urban Environments," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    8. Francesco Mancini & Gianluigi Lo Basso, 2020. "How Climate Change Affects the Building Energy Consumptions Due to Cooling, Heating, and Electricity Demands of Italian Residential Sector," Energies, MDPI, vol. 13(2), pages 1-24, January.
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    Cited by:

    1. Mateusz Płoszaj-Mazurek & Elżbieta Ryńska & Magdalena Grochulska-Salak, 2020. "Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design," Energies, MDPI, vol. 13(20), pages 1-19, October.
    2. Anosh Nadeem Butt & Branka Dimitrijević, 2022. "Multidisciplinary and Transdisciplinary Collaboration in Nature-Based Design of Sustainable Architecture and Urbanism," Sustainability, MDPI, vol. 14(16), pages 1-23, August.

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