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

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Improved modelling of electric loads for enabling demand response by applying physical and data-driven models : Project Response. / Koponen, Pekka; Hanninen, Seppo; Mutanen, Antti; Koskela, Juha; Rautiainen, Antti; Järventausta, Pertti; Niska, Harri; Kolehmainen, Mikko; Koivisto, Hannu.

2018 IEEE International Energy Conference, ENERGYCON 2018. IEEE, 2018. p. 1-6.

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

Harvard

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

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Author

Koponen, Pekka ; Hanninen, Seppo ; Mutanen, Antti ; Koskela, Juha ; Rautiainen, Antti ; Järventausta, Pertti ; Niska, Harri ; Kolehmainen, Mikko ; Koivisto, Hannu. / Improved modelling of electric loads for enabling demand response by applying physical and data-driven models : Project Response. 2018 IEEE International Energy Conference, ENERGYCON 2018. IEEE, 2018. pp. 1-6

Bibtex - Download

@inproceedings{89a81557e623442da6a4a730b70f7f7c,
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 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 = "6",
day = "27",
doi = "10.1109/ENERGYCON.2018.8398794",
language = "English",
pages = "1--6",
booktitle = "2018 IEEE International Energy Conference, ENERGYCON 2018",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Improved modelling of electric loads for enabling demand response by applying physical and data-driven models

T2 - Project Response

AU - Koponen, Pekka

AU - Hanninen, Seppo

AU - Mutanen, Antti

AU - Koskela, Juha

AU - Rautiainen, Antti

AU - Järventausta, Pertti

AU - Niska, Harri

AU - Kolehmainen, Mikko

AU - Koivisto, Hannu

PY - 2018/6/27

Y1 - 2018/6/27

N2 - 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.

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.

KW - Active demand

KW - Forecasting

KW - Hybrid models

KW - Machine learning

KW - Optimization

KW - Physically based models

U2 - 10.1109/ENERGYCON.2018.8398794

DO - 10.1109/ENERGYCON.2018.8398794

M3 - Conference contribution

SP - 1

EP - 6

BT - 2018 IEEE International Energy Conference, ENERGYCON 2018

PB - IEEE

ER -