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Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting

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  • Krzysztof Gajowniczek
  • Tomasz Ząbkowski

Abstract

Advanced metering infrastructures such as smart metering have begun to attract increasing attention; a considerable body of research is currently focusing on load profiling and forecasting at different scales on the grid. Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on the creation of more accurate electricity forecasts for residential customers. The contributions of this paper are threefold: using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; the optimal number of clusters for representing residential electricity use profiles is determined; and an extensive load forecasting study using different segmentation-enhanced forecasting algorithms is undertaken. Finally, from the operator’s perspective, the implications of the results are discussed in terms of the use of clustering methods for grouping electrical load patterns.

Suggested Citation

  • Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting," Complexity, Hindawi, vol. 2018, pages 1-21, April.
    • Handle: RePEc:hin:complx:3683969
    DOI: 10.1155/2018/3683969
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    References listed on IDEAS

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    Cited by:

    1. Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
    2. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
    3. Krzysztof Gajowniczek & Marcin Bator & Tomasz Ząbkowski & Arkadiusz Orłowski & Chu Kiong Loo, 2020. "Simulation Study on the Electricity Data Streams Time Series Clustering," Energies, MDPI, vol. 13(4), pages 1-25, February.

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