IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v47y2020i8p1474-1489.html
   My bibliography  Save this article

Data-driven planning support system for a campus design

Author

Listed:
  • Perry Pei-Ju Yang
  • Soowon Chang
  • Nirvik Saha
  • Helen W Chen

Abstract

The paper aims to develop a campus-level planning support system that is driven by data analytics by comparing two design approaches, anticipation and optimization. A campus is defined as a small-scale complex urban system of buildings and infrastructure. Three questions are addressed: (1) What generates campus design? What principles are taken for making design decisions? (2) How do we optimize design options based on multi-criteria performance and multi-objectives? (3) How can we manage a process of complex systems design, from scenario making, performance evaluation, design optimization to design generation? What properties can be derived from the above processes to inform campus design decisions? Driven by the above questions, design approaches by anticipation and by optimization were employed in a campus site design. By reviewing those processes, a data-driven campus planning support system is proposed to manage complex decisions and communicate design decisions through a visualization platform. This research will contribute to exploring how urban design is driven by data analytics for promoting energy efficiency and system resilience.

Suggested Citation

  • Perry Pei-Ju Yang & Soowon Chang & Nirvik Saha & Helen W Chen, 2020. "Data-driven planning support system for a campus design," Environment and Planning B, , vol. 47(8), pages 1474-1489, October.
  • Handle: RePEc:sae:envirb:v:47:y:2020:i:8:p:1474-1489
    DOI: 10.1177/2399808320910164
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2399808320910164
    Download Restriction: no

    File URL: https://libkey.io/10.1177/2399808320910164?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    2. Keirstead, James & Samsatli, Nouri & Shah, Nilay & Weber, Céline, 2012. "The impact of CHP (combined heat and power) planning restrictions on the efficiency of urban energy systems," Energy, Elsevier, vol. 41(1), pages 93-103.
    3. Best, Robert E. & Flager, Forest & Lepech, Michael D., 2015. "Modeling and optimization of building mix and energy supply technology for urban districts," Applied Energy, Elsevier, vol. 159(C), pages 161-177.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haozhi Pan & Stan Geertman & Brian Deal, 2020. "What does urban informatics add to planning support technology?," Environment and Planning B, , vol. 47(8), pages 1317-1325, October.
    2. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Elena Núñez Varela & Kristoffer Öhrling & Annika Moscati, 2022. "Analysis of the Challenges in the Swedish Urban Planning Process: A Case Study about Digitalization," Sustainability, MDPI, vol. 14(24), pages 1-14, December.

    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. Fuentes-Cortés, Luis Fabián & Flores-Tlacuahuac, Antonio, 2018. "Integration of distributed generation technologies on sustainable buildings," Applied Energy, Elsevier, vol. 224(C), pages 582-601.
    2. Yi, Ji Hyun & Ko, Woong & Park, Jong-Keun & Park, Hyeongon, 2018. "Impact of carbon emission constraint on design of small scale multi-energy system," Energy, Elsevier, vol. 161(C), pages 792-808.
    3. Kostevšek, Anja & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Papa, Gregor & Petek, Janez, 2016. "The concept of an ecosystem model to support the transformation to sustainable energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1460-1469.
    4. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Roosta, Alireza & Amiri, Babak, 2012. "A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch," Energy, Elsevier, vol. 42(1), pages 530-545.
    5. Li, Shunxi & Su, Bowen & St-Pierre, David L. & Sui, Pang-Chieh & Zhang, Guofang & Xiao, Jinsheng, 2017. "Decision-making of compressed natural gas station siting for public transportation: Integration of multi-objective optimization, fuzzy evaluating, and radar charting," Energy, Elsevier, vol. 140(P1), pages 11-17.
    6. Ma, Yixiang & Yu, Lean & Zhang, Guoxing & Lu, Zhiming & Wu, Jiaqian, 2023. "Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling," Renewable Energy, Elsevier, vol. 219(P1).
    7. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    8. Vinay Kumar Jadoun & G. Rahul Prashanth & Siddharth Suhas Joshi & Anshul Agarwal & Hasmat Malik & Majed A. Alotaibi & Abdulaziz Almutairi, 2021. "Optimal Scheduling of Non-Convex Cogeneration Units Using Exponentially Varying Whale Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-30, February.
    9. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
    10. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
    11. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    12. Fonseca, Juan D. & Commenge, Jean-Marc & Camargo, Mauricio & Falk, Laurent & Gil, Iván D., 2021. "Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach," Applied Energy, Elsevier, vol. 290(C).
    13. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    14. Weiwei Cui & Biao Lu, 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    15. Nour Eddine, A. & Chalet, D. & Faure, X. & Aixala, L. & Chessé, P., 2018. "Effect of engine exhaust gas pulsations on the performance of a thermoelectric generator for wasted heat recovery: An experimental and analytical investigation," Energy, Elsevier, vol. 162(C), pages 715-727.
    16. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    17. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    18. Alaia Sola & Cristina Corchero & Jaume Salom & Manel Sanmarti, 2018. "Simulation Tools to Build Urban-Scale Energy Models: A Review," Energies, MDPI, vol. 11(12), pages 1-24, November.
    19. Silverman, Rochelle E. & Flores, Robert J. & Brouwer, Jack, 2020. "Energy and economic assessment of distributed renewable gas and electricity generation in a small disadvantaged urban community," Applied Energy, Elsevier, vol. 280(C).
    20. Lourdes Uribe & Johan M Bogoya & Andrés Vargas & Adriana Lara & Günter Rudolph & Oliver Schütze, 2020. "A Set Based Newton Method for the Averaged Hausdorff Distance for Multi-Objective Reference Set Problems," Mathematics, MDPI, vol. 8(10), pages 1-29, October.

    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:sae:envirb:v:47:y:2020:i:8:p:1474-1489. 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: SAGE Publications (email available below). General contact details of provider: .

    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.