Hybrid model for short-term forecasting of loads and load control responses

Pekka Koponen, Harri Niska

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

    4 Citations (Scopus)


    A hybrid model for short-term forecasting of aggregated thermal loads and their load control responses is studied in this paper using field test data. Inputs include temperature measurement and forecast, measured power and control signals. The hybrid model comprises 1) partly physically based forecasting of the responses of the controlled thermal loads and the non-controlled power, and 2) forecasting the residual using Support Vector Machine (SVM). Their summation gives the hourly interval power forecast. Here partly physically based means that the model structure models thermal dynamics of houses. The response forecasting needs as inputs the daily heating energy demand and controllable power range, which are forecast by partly physically based models. Weekly load pattern is used to forecast the non-controllable power. SVM forecasts the residual. Alternative configurations are compared. The response forecasting model improved the forecasting accuracy most and adding the SVM for the residual further improved the accuracy.
    Original languageEnglish
    Title of host publicationPES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2016 IEEE
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-5090-3358-4, 978-1-5090-3357-7
    ISBN (Print)978-1-5090-3359-1
    Publication statusPublished - 2 Jul 2016
    MoE publication typeA4 Article in a conference publication
    EventIEEE PES Innovative Smart Grid Technologies, Europe - Ljubljana, Slovenia
    Duration: 9 Oct 201612 Oct 2016


    ConferenceIEEE PES Innovative Smart Grid Technologies, Europe
    Abbreviated titleISGT 2016


    • demand response
    • forecasting
    • hybrid models
    • machine learning
    • physical models


    Dive into the research topics of 'Hybrid model for short-term forecasting of loads and load control responses'. Together they form a unique fingerprint.

    Cite this