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

    1 Citation (Scopus)

    Abstract

    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
    DOIs
    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
    http://www.energycon2018.org/

    Conference

    ConferenceIEEE International Energy Conference, ENERGYCON 2018
    Abbreviated titleENERGYCON 2018
    CountryCyprus
    CityLimassol
    Period3/06/187/06/18
    Internet address

    Fingerprint

    Electric loads

    Keywords

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

    Cite this

    Koponen, P., Hänninen, S., Niska, H., Kolehmainen, M., Mutanen, A., Rautiainen, A., ... Koivisto, H. (2018). Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: project RESPONSE. In IEEE International Energy Conference ENERGYCON 2018: Towards Self-healing, Resilient and Green Electric Power and Energy Systems [19] IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/ENERGYCON.2018.8398794
    Koponen, Pekka ; Hänninen, Seppo ; Niska, Harri ; Kolehmainen, Mikko ; Mutanen, Antti ; Rautiainen, Antti ; Järventausta, Pertti ; Koivisto, Hannu. / Improved modelling of electric loads for enabling demand response by applying physical and data-driven models : project RESPONSE. IEEE International Energy Conference ENERGYCON 2018: Towards Self-healing, Resilient and Green Electric Power and Energy Systems. IEEE Institute of Electrical and Electronic Engineers , 2018.
    @inproceedings{fa9f328764ae4da8b152ff4bfc85d638,
    title = "Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: project RESPONSE",
    abstract = "Accurate load and response forecasts are a criticalenabler for high demand response penetrations and optimizationof responses and market actions. Project RESPONSE studies anddevelops methods to improve the forecasts. Its objectives are toimprove 1) load and response forecast and optimization modelsbased on both data- driven and physical modelling, and theirhybrid models, 2) utilization of various data sources such assmart metering data, weather data, measurements fromsubstations etc., and 3) performance criteria of load forecasting.The project applies, develops, compares, and integrates variousmodelling approaches including partly physical models, machinelearning, modern load profiling, autoregressive models, andKalman-filtering. It also applies non-linear constrainedoptimization to load responses. This paper gives an overview ofthe project and the results achieved so far.",
    keywords = "forecasting, machine learning, physically based models, hybrid models, active demand, optimization",
    author = "Pekka Koponen and Seppo H{\"a}nninen and Harri Niska and Mikko Kolehmainen and Antti Mutanen and Antti Rautiainen and Pertti J{\"a}rventausta and Hannu Koivisto",
    year = "2018",
    month = "6",
    day = "3",
    doi = "10.1109/ENERGYCON.2018.8398794",
    language = "English",
    isbn = "978-1-5386-1283-5",
    booktitle = "IEEE International Energy Conference ENERGYCON 2018",
    publisher = "IEEE Institute of Electrical and Electronic Engineers",
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    Koponen, P, Hänninen, S, Niska, H, Kolehmainen, M, Mutanen, A, Rautiainen, A, Järventausta, P & Koivisto, H 2018, Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: project RESPONSE. in IEEE International Energy Conference ENERGYCON 2018: Towards Self-healing, Resilient and Green Electric Power and Energy Systems., 19, IEEE Institute of Electrical and Electronic Engineers , IEEE International Energy Conference, ENERGYCON 2018, Limassol, Cyprus, 3/06/18. https://doi.org/10.1109/ENERGYCON.2018.8398794

    Improved modelling of electric loads for enabling demand response by applying physical and data-driven models : project RESPONSE. / Koponen, Pekka (Corresponding author); Hänninen, Seppo; Niska, Harri; Kolehmainen, Mikko; Mutanen, Antti; Rautiainen, Antti; Järventausta, Pertti; Koivisto, Hannu.

    IEEE International Energy Conference ENERGYCON 2018: Towards Self-healing, Resilient and Green Electric Power and Energy Systems. IEEE Institute of Electrical and Electronic Engineers , 2018. 19.

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

    TY - GEN

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    AU - Koponen, Pekka

    AU - Hänninen, Seppo

    AU - Niska, Harri

    AU - Kolehmainen, Mikko

    AU - Mutanen, Antti

    AU - Rautiainen, Antti

    AU - Järventausta, Pertti

    AU - Koivisto, Hannu

    PY - 2018/6/3

    Y1 - 2018/6/3

    N2 - Accurate load and response forecasts are a criticalenabler for high demand response penetrations and optimizationof responses and market actions. Project RESPONSE studies anddevelops methods to improve the forecasts. Its objectives are toimprove 1) load and response forecast and optimization modelsbased on both data- driven and physical modelling, and theirhybrid models, 2) utilization of various data sources such assmart metering data, weather data, measurements fromsubstations etc., and 3) performance criteria of load forecasting.The project applies, develops, compares, and integrates variousmodelling approaches including partly physical models, machinelearning, modern load profiling, autoregressive models, andKalman-filtering. It also applies non-linear constrainedoptimization to load responses. This paper gives an overview ofthe project and the results achieved so far.

    AB - Accurate load and response forecasts are a criticalenabler for high demand response penetrations and optimizationof responses and market actions. Project RESPONSE studies anddevelops methods to improve the forecasts. Its objectives are toimprove 1) load and response forecast and optimization modelsbased on both data- driven and physical modelling, and theirhybrid models, 2) utilization of various data sources such assmart metering data, weather data, measurements fromsubstations etc., and 3) performance criteria of load forecasting.The project applies, develops, compares, and integrates variousmodelling approaches including partly physical models, machinelearning, modern load profiling, autoregressive models, andKalman-filtering. It also applies non-linear constrainedoptimization to load responses. This paper gives an overview ofthe project and the results achieved so far.

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    KW - machine learning

    KW - physically based models

    KW - hybrid models

    KW - active demand

    KW - optimization

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    U2 - 10.1109/ENERGYCON.2018.8398794

    DO - 10.1109/ENERGYCON.2018.8398794

    M3 - Conference article in proceedings

    SN - 978-1-5386-1283-5

    BT - IEEE International Energy Conference ENERGYCON 2018

    PB - IEEE Institute of Electrical and Electronic Engineers

    ER -

    Koponen P, Hänninen S, Niska H, Kolehmainen M, Mutanen A, Rautiainen A et al. Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: project RESPONSE. In IEEE International Energy Conference ENERGYCON 2018: Towards Self-healing, Resilient and Green Electric Power and Energy Systems. IEEE Institute of Electrical and Electronic Engineers . 2018. 19 https://doi.org/10.1109/ENERGYCON.2018.8398794