Assessment of some methods for short-term load forecasting

Pekka Koponen, Antti Mutanen, Harri Niska

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

    4 Citations (Scopus)


    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 publicationIEEE PES Innovative Smart Grid Technologies, Europe
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages6
    ISBN (Electronic)978-1-4799-7720-8
    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

    Publication series

    SeriesIEEE PES Innovative Smart Grid Technologies Conference Europe


    Conference5th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014
    Abbreviated titleISGT-Europe 2014


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


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