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

Pekka Koponen; Seppo Hanninen; Antti Mutanen; Juha Koskela; Antti Rautiainen; Pertti Järventausta; Harri Niska; Mikko Kolehmainen; Hannu Koivisto

doi:10.1109/ENERGYCON.2018.8398794

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

Pekka Koponen, Seppo Hanninen, Antti Mutanen, Juha Koskela, Antti Rautiainen, Pertti Järventausta, Harri Niska, Mikko Kolehmainen, Hannu Koivisto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

5 Citations (Scopus)

67 Downloads (Pure)

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 language	English
Title of host publication	2018 IEEE International Energy Conference, ENERGYCON 2018
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538636695
DOIs	https://doi.org/10.1109/ENERGYCON.2018.8398794
Publication status	Published - 27 Jun 2018
Publication type	A4 Article in conference proceedings
Event	IEEE International Energy Conference - Limassol, Cyprus Duration: 3 Jun 2018 → 7 Jun 2018

Conference

Conference	IEEE International Energy Conference
Country/Territory	Cyprus
City	Limassol
Period	3/06/18 → 7/06/18

Keywords

Active demand
Forecasting
Hybrid models
Machine learning
Optimization
Physically based models

Publication forum classification

Publication forum level 0

ASJC Scopus subject areas

Artificial Intelligence
Energy Engineering and Power Technology
Control and Optimization

Access to Document

10.1109/ENERGYCON.2018.8398794

Koponen-ENERGYCON2018-final
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Accepted author manuscript, 617 KB

http://urn.fi/URN:NBN:fi:tty-201808172164

Cite this

Koponen, P., Hanninen, S., Mutanen, A., Koskela, J., Rautiainen, A., Järventausta, P., Niska, H., Kolehmainen, M., & Koivisto, H. (2018). Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: Project Response. In 2018 IEEE International Energy Conference, ENERGYCON 2018 (pp. 1-6). IEEE. https://doi.org/10.1109/ENERGYCON.2018.8398794

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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.",

keywords = "Active demand, Forecasting, Hybrid models, Machine learning, Optimization, Physically based models",

author = "Pekka Koponen and Seppo Hanninen and Antti Mutanen and Juha Koskela and Antti Rautiainen and Pertti J{\"a}rventausta and Harri Niska and Mikko Kolehmainen and Hannu Koivisto",

year = "2018",

month = jun,

day = "27",

doi = "10.1109/ENERGYCON.2018.8398794",

language = "English",

pages = "1--6",

booktitle = "2018 IEEE International Energy Conference, ENERGYCON 2018",

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Koponen, P, Hanninen, S, Mutanen, A, Koskela, J, Rautiainen, A, Järventausta, P, Niska, H, Kolehmainen, M & Koivisto, H 2018, Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: Project Response. in 2018 IEEE International Energy Conference, ENERGYCON 2018. IEEE, pp. 1-6, IEEE International Energy Conference, 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; Hanninen, Seppo; Mutanen, Antti et al.
2018 IEEE International Energy Conference, ENERGYCON 2018. IEEE, 2018. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AU - Koponen, Pekka

AU - Hanninen, Seppo

AU - Mutanen, Antti

AU - Koskela, Juha

AU - Rautiainen, Antti

AU - Järventausta, Pertti

AU - Niska, Harri

AU - Kolehmainen, Mikko

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AB - 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.

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KW - Hybrid models

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KW - Optimization

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

Abstract

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Keywords

Publication forum classification

ASJC Scopus subject areas

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