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 proceedingConference contribution

6 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 languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728106526
DOIs
Publication statusPublished - Jun 2019
Event19th 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 201914 Jun 2019

Conference

Conference19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
CountryItaly
CityGenoa
Period11/06/1914/06/19

Keywords

  • electricity consumption
  • flexibility
  • home energy management
  • machine learning
  • regression models

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  • 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 [8783634] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EEEIC.2019.8783634