Forecasting the responses of market based control of residential electrical heating loads

Pekka Koponen, Pekka Takki

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

    Abstract

    The value of demand responses depends much on their predictability. In Helsinki dynamic smart metering based dynamic demand response can control about 35 MW of residential full storage heating. A partly physically based model for forecasting this aggregated load and its responses to control signals and ambient air temperature is being developed. Also interval measured consumption is used as model input. The initial results show that the control response model developed improves the forecasting accuracy very much. This research and its initial results are reported in this paper.
    Original languageEnglish
    Title of host publicationChallenges of Implementing Active Distribution System Management
    Subtitle of host publicationCIRED Workshop 2014
    PublisherInternational Conference and Exhibition on Electricity Distribution CIRED
    Number of pages5
    Publication statusPublished - 2014
    MoE publication typeNot Eligible
    EventCIRED Workshop 2014 - Rome, Italy
    Duration: 11 Jun 201412 Jun 2014

    Conference

    ConferenceCIRED Workshop 2014
    CountryItaly
    CityRome
    Period11/06/1412/06/14

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    Heating
    Air
    Temperature

    Keywords

    • electricity consumption
    • forecasting
    • demand response
    • heating

    Cite this

    Koponen, P., & Takki, P. (2014). Forecasting the responses of market based control of residential electrical heating loads. In Challenges of Implementing Active Distribution System Management: CIRED Workshop 2014 [0178] International Conference and Exhibition on Electricity Distribution CIRED.
    Koponen, Pekka ; Takki, Pekka. / Forecasting the responses of market based control of residential electrical heating loads. Challenges of Implementing Active Distribution System Management: CIRED Workshop 2014. International Conference and Exhibition on Electricity Distribution CIRED, 2014.
    @inproceedings{0f4b7bd549e8422c85dbe1ed4dd7bac7,
    title = "Forecasting the responses of market based control of residential electrical heating loads",
    abstract = "The value of demand responses depends much on their predictability. In Helsinki dynamic smart metering based dynamic demand response can control about 35 MW of residential full storage heating. A partly physically based model for forecasting this aggregated load and its responses to control signals and ambient air temperature is being developed. Also interval measured consumption is used as model input. The initial results show that the control response model developed improves the forecasting accuracy very much. This research and its initial results are reported in this paper.",
    keywords = "electricity consumption, forecasting, demand response, heating",
    author = "Pekka Koponen and Pekka Takki",
    note = "CA2: BA2135",
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    Koponen, P & Takki, P 2014, Forecasting the responses of market based control of residential electrical heating loads. in Challenges of Implementing Active Distribution System Management: CIRED Workshop 2014., 0178, International Conference and Exhibition on Electricity Distribution CIRED, CIRED Workshop 2014, Rome, Italy, 11/06/14.

    Forecasting the responses of market based control of residential electrical heating loads. / Koponen, Pekka; Takki, Pekka.

    Challenges of Implementing Active Distribution System Management: CIRED Workshop 2014. International Conference and Exhibition on Electricity Distribution CIRED, 2014. 0178.

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

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    N2 - The value of demand responses depends much on their predictability. In Helsinki dynamic smart metering based dynamic demand response can control about 35 MW of residential full storage heating. A partly physically based model for forecasting this aggregated load and its responses to control signals and ambient air temperature is being developed. Also interval measured consumption is used as model input. The initial results show that the control response model developed improves the forecasting accuracy very much. This research and its initial results are reported in this paper.

    AB - The value of demand responses depends much on their predictability. In Helsinki dynamic smart metering based dynamic demand response can control about 35 MW of residential full storage heating. A partly physically based model for forecasting this aggregated load and its responses to control signals and ambient air temperature is being developed. Also interval measured consumption is used as model input. The initial results show that the control response model developed improves the forecasting accuracy very much. This research and its initial results are reported in this paper.

    KW - electricity consumption

    KW - forecasting

    KW - demand response

    KW - heating

    M3 - Conference article in proceedings

    BT - Challenges of Implementing Active Distribution System Management

    PB - International Conference and Exhibition on Electricity Distribution CIRED

    ER -

    Koponen P, Takki P. Forecasting the responses of market based control of residential electrical heating loads. In Challenges of Implementing Active Distribution System Management: CIRED Workshop 2014. International Conference and Exhibition on Electricity Distribution CIRED. 2014. 0178