Assessment of some methods for short-term load forecasting

Pekka Koponen, A. Mutanen, Harri Niska

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    4 Citations (Scopus)

    Abstract

    Accurate forecasting of loads is essential for smart grids and energy markets. This paper compares the performance of the following models in short-term load forecasting: 1) smart metering data based profile models, 2) a neural network (NN) model, and 3) a Kalman-filter based predictor with input nonlinearities and a physically based main structure. The comparison helps method selection for the development of hybrid models for forecasting the load control responses. According to the results all these three modeling approaches show much better performance than 4) the traditional load profiles and 5) a static outdoor temperature dependency model applied with a lag. The neural network model was the most accurate in the comparison, but the differences of the three methods developed were rather small and also other aspects and other methods must be considered and compared when selecting the method for a specific purpose.
    Original languageEnglish
    Title of host publicationProceedings
    Subtitle of host publicationIEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages6
    ISBN (Electronic)978-1-4799-7720-8
    DOIs
    Publication statusPublished - 2014
    MoE publication typeA4 Article in a conference publication
    Event5th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014 - Istanbul, Turkey
    Duration: 12 Oct 201415 Oct 2014

    Conference

    Conference5th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014
    Abbreviated titleISGT-Europe 2014
    CountryTurkey
    CityIstanbul
    Period12/10/1415/10/14

    Keywords

    • power demand
    • demand forecasting
    • load modeling
    • prediction algorithms
    • artificial neural networks

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  • Cite this

    Koponen, P., Mutanen, A., & Niska, H. (2014). Assessment of some methods for short-term load forecasting. In Proceedings: IEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014 IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/ISGTEurope.2014.7028901