Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model

Roya Ahmadiahangar; Tobias Häring; Argo Rosin; Tarmo Korõtko; João Martins

doi:10.1109/EEEIC.2019.8783634

Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model

Roya Ahmadiahangar, Tobias Häring, Argo Rosin, Tarmo Korõtko, João Martins

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

38 Citations (Scopus)

Abstract

Flexibility is already an important concept in the context of electricity balancing. Residential customers' consumptions are usually highly volatile and depend on individual behaviour; hence, it complicates forecasts and flexibility extractions. This paper proposes the use of machine learning based regression models to generate load patterns for forecasting the potential flexibility of residential customers and improving both technical and economic smart grid operations. The advantage of proposed method is that it can be used, in on-line and real-time methods, in a wide range of control approaches.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9781728106526
DOIs	https://doi.org/10.1109/EEEIC.2019.8783634
Publication status	Published - Jun 2019
Event	19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 - Genoa, Italy Duration: 11 Jun 2019 → 14 Jun 2019

Conference

Conference	19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
Country/Territory	Italy
City	Genoa
Period	11/06/19 → 14/06/19

Keywords

electricity consumption
flexibility
home energy management
machine learning
regression models

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/EEEIC.2019.8783634

Cite this

Ahmadiahangar, R., Häring, T., Rosin, A., Korõtko, T., & Martins, J. (2019). Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model. In Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 Article 8783634 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/EEEIC.2019.8783634

Ahmadiahangar, Roya ; Häring, Tobias ; Rosin, Argo et al. / Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model. Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019. Institute of Electrical and Electronics Engineers (IEEE), 2019.

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abstract = "Flexibility is already an important concept in the context of electricity balancing. Residential customers' consumptions are usually highly volatile and depend on individual behaviour; hence, it complicates forecasts and flexibility extractions. This paper proposes the use of machine learning based regression models to generate load patterns for forecasting the potential flexibility of residential customers and improving both technical and economic smart grid operations. The advantage of proposed method is that it can be used, in on-line and real-time methods, in a wide range of control approaches.",

keywords = "electricity consumption, flexibility, home energy management, machine learning, regression models",

author = "Roya Ahmadiahangar and Tobias H{\"a}ring and Argo Rosin and Tarmo Kor{\~o}tko and Jo{\~a}o Martins",

note = "This work was supported by the Estonian Research Council grant PUT (PUT1680). and Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts ZEBE, grant 2014-2020.4.01.15-0016 funded by European Regional Development Fund.; 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 ; Conference date: 11-06-2019 Through 14-06-2019",

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month = jun,

doi = "10.1109/EEEIC.2019.8783634",

language = "English",

booktitle = "Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019",

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Ahmadiahangar, R, Häring, T, Rosin, A, Korõtko, T & Martins, J 2019, Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model. in Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019., 8783634, Institute of Electrical and Electronics Engineers (IEEE), 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019, Genoa, Italy, 11/06/19. https://doi.org/10.1109/EEEIC.2019.8783634

Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model. / Ahmadiahangar, Roya; Häring, Tobias; Rosin, Argo et al.
Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019. Institute of Electrical and Electronics Engineers (IEEE), 2019. 8783634.

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

TY - GEN

T1 - Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model

AU - Ahmadiahangar, Roya

AU - Häring, Tobias

AU - Rosin, Argo

AU - Korõtko, Tarmo

AU - Martins, João

N1 - This work was supported by the Estonian Research Council grant PUT (PUT1680). and Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts ZEBE, grant 2014-2020.4.01.15-0016 funded by European Regional Development Fund.

PY - 2019/6

Y1 - 2019/6

N2 - Flexibility is already an important concept in the context of electricity balancing. Residential customers' consumptions are usually highly volatile and depend on individual behaviour; hence, it complicates forecasts and flexibility extractions. This paper proposes the use of machine learning based regression models to generate load patterns for forecasting the potential flexibility of residential customers and improving both technical and economic smart grid operations. The advantage of proposed method is that it can be used, in on-line and real-time methods, in a wide range of control approaches.

AB - Flexibility is already an important concept in the context of electricity balancing. Residential customers' consumptions are usually highly volatile and depend on individual behaviour; hence, it complicates forecasts and flexibility extractions. This paper proposes the use of machine learning based regression models to generate load patterns for forecasting the potential flexibility of residential customers and improving both technical and economic smart grid operations. The advantage of proposed method is that it can be used, in on-line and real-time methods, in a wide range of control approaches.

KW - electricity consumption

KW - flexibility

KW - home energy management

KW - machine learning

KW - regression models

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M3 - Conference contribution

BT - Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019

Y2 - 11 June 2019 through 14 June 2019

ER -

Ahmadiahangar R, Häring T, Rosin A, Korõtko T, Martins J. Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model. In Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019. Institute of Electrical and Electronics Engineers (IEEE). 2019. 8783634 doi: 10.1109/EEEIC.2019.8783634

Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model

Abstract

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Keywords

UN SDGs

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