Solar generation forecasting by Recurrent Neural Networks optimized by Levenberg-Marquardt Algorithm

Shahid M. Awan, Zubair A. Khan, Muhammad Aslam

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

9 Citations (Scopus)

Abstract

Solar photovoltaic systems convert the solar energy into electric power. Many factors affect the solar power generation through solar cells. The factors include atmospheric conditions, trajectory of sun, weather conditions, cloud cover and the physical properties of solar energy plant that converts solar energy to electric power. The output power is mainly influenced by the incoming radiation and the characteristics of solar panel. Accurate and correct knowledge about these factors guarantees a reliable solar generation forecasting model. This paper proposes a solution for solar power generation forecasting by incorporating the effecting parameters with the use of Recurrent Neural Network (RNN)model. The RNN is further optimized by Levenberg-Marquardt Algorithm (LMA)to get better accuracy of forecasts. The obtained results confirm the suitability of proposed approach.
Original languageEnglish
Title of host publicationIECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
Place of PublicationPiscataway, NJ
PublisherIEEE
ISBN (Electronic)9781509066841
DOIs
Publication statusPublished - 31 Dec 2018

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