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

Fingerprint

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.
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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.",
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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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PY - 2014

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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.

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BT - Challenges of Implementing Active Distribution System Management

PB - International Conference and Exhibition on Electricity Distribution CIRED

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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