IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v355y2024ics0306261923016343.html
   My bibliography  Save this article

Experiences from developing an open urban data portal for collaborative research and innovation

Author

Listed:
  • Krayem, Alaa
  • Thorin, Eva
  • Wallin, Fredrik

Abstract

The energy transition towards sustainable resources is more urgent than ever given the environmental and geopolitical challenges. Being one of the major energy users, cities need to understand their energy sector to accomplish its transition, by means of data. However, data are not easily accessible and have their own challenges. This paper presents a joint effort between researchers, city representatives and industry to provide an urban system service that supports research, accelerates urban innovation, and involves the community. An energy data portal, “NRGYHUB”, has been developed, where hourly data from thousands of energy meters are available. These meters were collected from neighborhoods in the city of Västerås, Sweden, and they measure electrical and heating energy. In addition, the data are complemented by geometrical and non-geometrical information of the buildings, as well as demographic statistics of the areas. The paper describes the process of data collection, preprocessing, and visualization, in addition to the main challenges and limitations of the project. This dataset can be used for energy use benchmarking, prediction, and analysis.

Suggested Citation

  • Krayem, Alaa & Thorin, Eva & Wallin, Fredrik, 2024. "Experiences from developing an open urban data portal for collaborative research and innovation," Applied Energy, Elsevier, vol. 355(C).
  • Handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923016343
    DOI: 10.1016/j.apenergy.2023.122270
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923016343
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.122270?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Ying & Campana, Pietro Elia & Stridh, Bengt & Yan, Jinyue, 2020. "Potential analysis of roof-mounted solar photovoltaics in Sweden," Applied Energy, Elsevier, vol. 279(C).
    2. Mathew, Paul A. & Dunn, Laurel N. & Sohn, Michael D. & Mercado, Andrea & Custudio, Claudine & Walter, Travis, 2015. "Big-data for building energy performance: Lessons from assembling a very large national database of building energy use," Applied Energy, Elsevier, vol. 140(C), pages 85-93.
    3. Vishanth Weerakkody & Zahir Irani & Kawal Kapoor & Uthayasankar Sivarajah & Yogesh K. Dwivedi, 2017. "Open data and its usability: an empirical view from the Citizen’s perspective," Information Systems Frontiers, Springer, vol. 19(2), pages 285-300, April.
    Full references (including those not matched with items on IDEAS)

    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. Formolli, M. & Kleiven, T. & Lobaccaro, G., 2023. "Assessing solar energy accessibility at high latitudes: A systematic review of urban spatial domains, metrics, and parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    2. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Lai, Yuan & Papadopoulos, Sokratis & Fuerst, Franz & Pivo, Gary & Sagi, Jacob & Kontokosta, Constantine E., 2022. "Building retrofit hurdle rates and risk aversion in energy efficiency investments," Applied Energy, Elsevier, vol. 306(PB).
    4. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    5. Zhou, Kaile & Yang, Shanlin, 2015. "A framework of service-oriented operation model of China׳s power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 719-725.
    6. Robinson, Caleb & Dilkina, Bistra & Hubbs, Jeffrey & Zhang, Wenwen & Guhathakurta, Subhrajit & Brown, Marilyn A. & Pendyala, Ram M., 2017. "Machine learning approaches for estimating commercial building energy consumption," Applied Energy, Elsevier, vol. 208(C), pages 889-904.
    7. Ren, Haoshan & Ma, Zhenjun & Chan, Antoni B. & Sun, Yongjun, 2023. "Optimal planning of municipal-scale distributed rooftop photovoltaic systems with maximized solar energy generation under constraints in high-density cities," Energy, Elsevier, vol. 263(PA).
    8. Kalliopi G. Droutsa & Constantinos A. Balaras & Spyridon Lykoudis & Simon Kontoyiannidis & Elena G. Dascalaki & Athanassios A. Argiriou, 2020. "Baselines for Energy Use and Carbon Emission Intensities in Hellenic Nonresidential Buildings," Energies, MDPI, vol. 13(8), pages 1-29, April.
    9. Majidpour, Mostafa & Qiu, Charlie & Chu, Peter & Pota, Hemanshu R. & Gadh, Rajit, 2016. "Forecasting the EV charging load based on customer profile or station measurement?," Applied Energy, Elsevier, vol. 163(C), pages 134-141.
    10. Kwonsik Song & Kyle Anderson & SangHyun Lee & Kaitlin T. Raimi & P. Sol Hart, 2020. "Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data," Energies, MDPI, vol. 13(14), pages 1-21, July.
    11. Ahn, Jonghoon & Cho, Soolyeon & Chung, Dae Hun, 2016. "Development of a statistical analysis model to benchmark the energy use intensity of subway stations," Applied Energy, Elsevier, vol. 179(C), pages 488-496.
    12. Guglielmina Mutani & Valeria Todeschi, 2021. "Optimization of Costs and Self-Sufficiency for Roof Integrated Photovoltaic Technologies on Residential Buildings," Energies, MDPI, vol. 14(13), pages 1-25, July.
    13. Federico Cabitza & Angela Locoro & Carlo Batini, 2020. "Making Open Data more Personal Through a Social Value Perspective: a Methodological Approach," Information Systems Frontiers, Springer, vol. 22(1), pages 131-148, February.
    14. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    15. Marijn Janssen & David Konopnicki & Jane L. Snowdon & Adegboyega Ojo, 2017. "Driving public sector innovation using big and open linked data (BOLD)," Information Systems Frontiers, Springer, vol. 19(2), pages 189-195, April.
    16. Byeongjoon Noh & Juntae Son & Hansaem Park & Seongju Chang, 2017. "In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining," Sustainability, MDPI, vol. 9(11), pages 1-20, November.
    17. Talukder, Md Shamim & Shen, Liang & Hossain Talukder, Md Farid & Bao, Yukun, 2019. "Determinants of user acceptance and use of open government data (OGD): An empirical investigation in Bangladesh," Technology in Society, Elsevier, vol. 56(C), pages 147-156.
    18. Berardi, Umberto, 2017. "A cross-country comparison of the building energy consumptions and their trends," Resources, Conservation & Recycling, Elsevier, vol. 123(C), pages 230-241.
    19. Gökhan Demirdöğen & Zeynep Işık & Yusuf Arayici, 2020. "Lean Management Framework for Healthcare Facilities Integrating BIM, BEPS and Big Data Analytics," Sustainability, MDPI, vol. 12(17), pages 1-33, August.
    20. Hyungjun Seo & Seunghwan Myeong, 2021. "Determinant Factors for Adoption of Government as a Platform in South Korea: Mediating Effects on the Perception of Intelligent Information Technology," Sustainability, MDPI, vol. 13(18), pages 1-20, September.

    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:eee:appene:v:355:y:2024:i:c:s0306261923016343. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.