Now-casting photovoltaic power with wavelet analysis and extreme learning machines

A. Teneketzoglou, N.G. Paterakis, J.P.S. Catalão

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

1 Citation (Scopus)

Abstract

High penetration of Photovoltaic (PV) systems, a variable resource, poses challenges to the stability and power quality of electrical grids. Forecasting accurately the PV power has been recognized as a way to ease this problem. This work addresses now-casting of PV power with Extreme Learning Machines (ELMs) without exogenous inputs. Wavelet decomposition and multi-resolution analysis is the most effective way to achieve high accuracy for 5 min-ahead forecast up to 70% greater than the persistence model. A neural network evaluation algorithm based on multiple initializations and incremental hidden nodes is applied and ELMs performance and computation efficiency is evaluated versus Time Delay Neural Networks (TDNNs) for time and time-frequency domain forecasting.

Original languageEnglish
Title of host publication2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-5090-0191-0
DOIs
Publication statusPublished - 11 Sep 2015
Externally publishedYes
Event18th International Conference on Intelligent System Application to Power Systems (ISAP 2015) - Porto, Portugal
Duration: 11 Sep 201517 Sep 2015
Conference number: 18

Conference

Conference18th International Conference on Intelligent System Application to Power Systems (ISAP 2015)
Abbreviated titleISAP 2015
CountryPortugal
CityPorto
Period11/09/1517/09/15

Keywords

  • forecasting
  • neural networks
  • photovoltaic power

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