Assessment of some methods for short-term load forecasting

Pekka Koponen, A. Mutanen, Harri Niska

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

4 Citations (Scopus)

Abstract

Accurate forecasting of loads is essential for smart grids and energy markets. This paper compares the performance of the following models in short-term load forecasting: 1) smart metering data based profile models, 2) a neural network (NN) model, and 3) a Kalman-filter based predictor with input nonlinearities and a physically based main structure. The comparison helps method selection for the development of hybrid models for forecasting the load control responses. According to the results all these three modeling approaches show much better performance than 4) the traditional load profiles and 5) a static outdoor temperature dependency model applied with a lag. The neural network model was the most accurate in the comparison, but the differences of the three methods developed were rather small and also other aspects and other methods must be considered and compared when selecting the method for a specific purpose.
Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014
PublisherInstitute of Electrical and Electronic Engineers IEEE
Number of pages6
ISBN (Electronic)978-1-4799-7720-8
DOIs
Publication statusPublished - 2014
MoE publication typeA4 Article in a conference publication
Event5th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014 - Istanbul, Turkey
Duration: 12 Oct 201415 Oct 2014

Conference

Conference5th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014
Abbreviated titleISGT-Europe 2014
CountryTurkey
CityIstanbul
Period12/10/1415/10/14

Fingerprint

Neural networks
Control nonlinearities
Kalman filters
Temperature

Keywords

  • power demand
  • demand forecasting
  • load modeling
  • prediction algorithms
  • artificial neural networks

Cite this

Koponen, P., Mutanen, A., & Niska, H. (2014). Assessment of some methods for short-term load forecasting. In Proceedings: IEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014 Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/ISGTEurope.2014.7028901
Koponen, Pekka ; Mutanen, A. ; Niska, Harri. / Assessment of some methods for short-term load forecasting. Proceedings: IEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014. Institute of Electrical and Electronic Engineers IEEE, 2014.
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Koponen, P, Mutanen, A & Niska, H 2014, Assessment of some methods for short-term load forecasting. in Proceedings: IEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014. Institute of Electrical and Electronic Engineers IEEE, 5th IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2014, Istanbul, Turkey, 12/10/14. https://doi.org/10.1109/ISGTEurope.2014.7028901

Assessment of some methods for short-term load forecasting. / Koponen, Pekka; Mutanen, A.; Niska, Harri.

Proceedings: IEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014. Institute of Electrical and Electronic Engineers IEEE, 2014.

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

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Koponen P, Mutanen A, Niska H. Assessment of some methods for short-term load forecasting. In Proceedings: IEEE PES Innovative Smart Grid Technologies, Europe, ISGT-Europe, 2014. Institute of Electrical and Electronic Engineers IEEE. 2014 https://doi.org/10.1109/ISGTEurope.2014.7028901