Prediction interval estimation of 10 second fluctuation of PV output with just-in-time modeling

Nao Kumekawa, Hayato Honma, Shinji Wakao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Output of photovoltaic (PV) systems depends on weather conditions. Therefore if there is a large introduction of PV systems, the power quality in the distribution system will be affected. One effective solution for this problem is to predict PV output. Although the need for prediction information for short period fluctuation is increasing, it is difficult to directly predict a steep fluctuation on the second time scale. For the prediction information of PV output, we propose the estimation of the prediction interval of the fluctuation widths on a 10 second scale. In this paper, we carry out the prediction by using the conventional method, with one-dimensional kernel density estimation, and the proposed method, with two-dimensional kernel density estimation. Then, we discuss the effectiveness of the proposed method based on several numerical indexes.

Original languageEnglish
Title of host publication2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3494-3499
Number of pages6
ISBN (Electronic)9781509056057
DOIs
Publication statusPublished - 2017
Event44th IEEE Photovoltaic Specialist Conference, PVSC 2017 - Washington, United States
Duration: 2017 Jun 252017 Jun 30

Publication series

Name2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017

Other

Other44th IEEE Photovoltaic Specialist Conference, PVSC 2017
CountryUnited States
CityWashington
Period17/6/2517/6/30

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

Fingerprint Dive into the research topics of 'Prediction interval estimation of 10 second fluctuation of PV output with just-in-time modeling'. Together they form a unique fingerprint.

Cite this