Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: project RESPONSE

Pekka Koponen (Corresponding author), Seppo Hänninen, Harri Niska, Mikko Kolehmainen, Antti Mutanen, Antti Rautiainen, Pertti Järventausta, Hannu Koivisto

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

    5 Citations (Scopus)


    Accurate load and response forecasts are a critical
    enabler for high demand response penetrations and optimization
    of responses and market actions. Project RESPONSE studies and
    develops methods to improve the forecasts. Its objectives are to
    improve 1) load and response forecast and optimization models
    based on both data- driven and physical modelling, and their
    hybrid models, 2) utilization of various data sources such as
    smart metering data, weather data, measurements from
    substations etc., and 3) performance criteria of load forecasting.
    The project applies, develops, compares, and integrates various
    modelling approaches including partly physical models, machine
    learning, modern load profiling, autoregressive models, and
    Kalman-filtering. It also applies non-linear constrained
    optimization to load responses. This paper gives an overview of
    the project and the results achieved so far.
    Original languageEnglish
    Title of host publicationIEEE International Energy Conference ENERGYCON 2018
    Subtitle of host publicationTowards Self-healing, Resilient and Green Electric Power and Energy Systems
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages6
    ISBN (Electronic)978-1-5386-3669-5
    ISBN (Print)978-1-5386-1283-5
    Publication statusPublished - 3 Jun 2018
    MoE publication typeA4 Article in a conference publication
    EventIEEE International Energy Conference, ENERGYCON 2018 - Limassol, Cyprus
    Duration: 3 Jun 20187 Jun 2018


    ConferenceIEEE International Energy Conference, ENERGYCON 2018
    Abbreviated titleENERGYCON 2018
    Internet address


    • forecasting
    • machine learning
    • physically based models
    • hybrid models
    • active demand
    • optimization


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