Effective Input Dataset Identification Methodology for Accurate Prediction of Local PV Power Production

Abinet Tesfaye Eseye, Matti Lehtonen, Toni Tukia, Semen Uimonen, Robert Millar

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

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Local photovoltaic (PV) systems are playing a considerable role globally as a power resource and constituent element of the smart grid. Nevertheless, PVs may cause significant problems to the electric grid. This is due to the high variability of the PV power that is instigated by intermittent environmental conditions. Accurate prediction of PV power is very important to operate power grids containing high penetration of PVs. Most prior approaches have focused on forecasting the collective quantity of solar power generation at national or regional scale and disregarded the local PVs that are installed mainly for local electric supply. This paper devises an effective input dataset identification methodology (IDIM) to find the most significant and non-repetitive input variables for accurate prediction of the power production of local PVs. In the devised methodology, the Binary Genetic Algorithm (BGA) is used for the input variable identification and Support Vector Regression (SVR) is employed for evaluating the fitness of the input datasets. The devised methodology is implemented and validated based on actual local PVs (building rooftop PVs) located in the Otaniemi area of Espoo, Finland. The results are compared with those obtained by conventional counterparts and manifest outperformed performances.
Original languageEnglish
Title of host publicationProceedings of the IEEE PES Europe Conference on Innovative Smart Grid Technologies, ISGT-Europe 2019
Number of pages5
ISBN (Electronic)978-1-5386-8218-0
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventIEEE PES Europe Conference on Innovative Smart Grid Technologies - University POLITEHNICA of Bucharest, Bucharest, Romania
Duration: 29 Sep 20192 Oct 2019

Publication series

NameIEEE PES Innovative Smart Grid Technologies Conference Europe
ISSN (Print)2165-4816
ISSN (Electronic)2165-4824


ConferenceIEEE PES Europe Conference on Innovative Smart Grid Technologies
Abbreviated titleISGT-Europe
City Bucharest
Internet address


  • BGA
  • Fitness evaluation measure
  • Input dataset identification
  • Local PV
  • Prediction
  • Renewable energy
  • Smart grid
  • SVR


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