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Reducing energy consumption by using self-organizing maps to create more personalized electricity use information

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  • Räsänen, Teemu
  • Ruuskanen, Juhani
  • Kolehmainen, Mikko

Abstract

Identification of electricity use is one of the key elements to motivate customers to promote activities leading more efficient use of energy. Furthermore, electricity use comparisons with other similar customers give more interesting and concrete point of view to examine own consumption habits. In future, electricity providers and retailers are willing and probably forced by legislation to provide such information by the means of energy conservation and efficiency improvement. On the other hand, high number of customers set challenges to handle electricity use data and to create proper comparison information. In this study we present efficient and highly automated way to create comparison groups based on customers building characteristics. The main advantages of the data-based approach are that customer location is noticed, comparison groups are created using concrete building information, data processing is highly automated and also method is computationally efficient. Additionally, presented method provide tool to target and to create customer specific electricity saving guidance. The performance of suggested approach was tested using data set which contained electricity use and building information concerning almost 8000 customers.

Suggested Citation

  • Räsänen, Teemu & Ruuskanen, Juhani & Kolehmainen, Mikko, 2008. "Reducing energy consumption by using self-organizing maps to create more personalized electricity use information," Applied Energy, Elsevier, vol. 85(9), pages 830-840, September.
  • Handle: RePEc:eee:appene:v:85:y:2008:i:9:p:830-840
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    References listed on IDEAS

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    3. Prince, Stephen, 2005. "Customers: The "New" Strategic Asset for Utilities," The Electricity Journal, Elsevier, vol. 18(1), pages 85-90.
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    5. Qiu, Dawei & Wang, Yi & Wang, Junkai & Jiang, Chuanwen & Strbac, Goran, 2023. "Personalized retail pricing design for smart metering consumers in electricity market," Applied Energy, Elsevier, vol. 348(C).
    6. Miller, Clayton & Nagy, Zoltán & Schlueter, Arno, 2018. "A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1365-1377.
    7. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
    8. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    9. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2017. "k-means based load estimation of domestic smart meter measurements," Applied Energy, Elsevier, vol. 194(C), pages 333-342.
    10. Räsänen, Teemu & Voukantsis, Dimitrios & Niska, Harri & Karatzas, Kostas & Kolehmainen, Mikko, 2010. "Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data," Applied Energy, Elsevier, vol. 87(11), pages 3538-3545, November.
    11. Eunjung Lee & Jinho Kim & Dongsik Jang, 2020. "Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study," Energies, MDPI, vol. 13(6), pages 1-18, March.
    12. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2016. "State estimation of medium voltage distribution networks using smart meter measurements," Applied Energy, Elsevier, vol. 184(C), pages 207-218.
    13. Mahmoud, Mohamed A. & Alajmi, Ali F., 2010. "Quantitative assessment of energy conservation due to public awareness campaigns using neural networks," Applied Energy, Elsevier, vol. 87(1), pages 220-228, January.
    14. Heikkinen, M. & Poutiainen, H. & Liukkonen, M. & Heikkinen, T. & Hiltunen, Y., 2011. "Subtraction analysis based on self-organizing maps for an industrial wastewater treatment process," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(3), pages 450-459.
    15. Liukkonen, M. & Hiltunen, T., 2014. "Adaptive monitoring of emissions in energy boilers using self-organizing maps: An application to a biomass-fired CFB (circulating fluidized bed)," Energy, Elsevier, vol. 73(C), pages 443-452.
    16. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    17. Anderson, Kyle & Song, Kwonsik & Lee, SangHyun & Krupka, Erin & Lee, Hyunsoo & Park, Moonseo, 2017. "Longitudinal analysis of normative energy use feedback on dormitory occupants," Applied Energy, Elsevier, vol. 189(C), pages 623-639.
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    19. Anderson, Kyle & Lee, SangHyun, 2016. "An empirically grounded model for simulating normative energy use feedback interventions," Applied Energy, Elsevier, vol. 173(C), pages 272-282.

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