ANN based prognostication of the PV panel output power under various environmental conditions

Shady Khalil, Omar H. Abu-Rub, Hazem Nounou

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

2 Citations (Scopus)

Abstract

The modules of the photovoltaic (PV) generation system convert solar energy into direct current (dc) electricity. Many complex factors, such as temperature and dust, influence PV arrays operation, making it difficult to ensure the optimal utilization of the solar energy. Achieving maximum power output under all possible system operation conditions is an important target. This paper proposes the possibility of developing a reliable relationship between the PV system power generation and efficiency, and various environmental factors such as solar irradiance, temperature, dust, and wind, using artificial neural network (ANN). The study is considering different prediction horizons to identify the influence of climate variability on power output and efficiency of the PV modules and to maximize the system usability. The proposed system does not require any physical definitions of the modules in order to predict power output under varying weather conditions. Experimental implementation is conducted to demonstrate the effectiveness of the proposed system.

Original languageEnglish
Title of host publication2018 IEEE Texas Power and Energy Conference, TPEC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
Volume2018-February
ISBN (Electronic)9781538610060
DOIs
Publication statusPublished - 9 Mar 2018
Event2nd IEEE Texas Power and Energy Conference, TPEC 2018 - College Station, United States
Duration: 8 Feb 20189 Feb 2018

Other

Other2nd IEEE Texas Power and Energy Conference, TPEC 2018
CountryUnited States
CityCollege Station
Period8/2/189/2/18

Fingerprint

Solar energy
Dust
Neural networks
Power generation
Electricity
Temperature

Keywords

  • Artificial Neural Network
  • Environmental conditions
  • Maximum power
  • Photovoltaic Module

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

Cite this

Khalil, S., Abu-Rub, O. H., & Nounou, H. (2018). ANN based prognostication of the PV panel output power under various environmental conditions. In 2018 IEEE Texas Power and Energy Conference, TPEC 2018 (Vol. 2018-February, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TPEC.2018.8312051

ANN based prognostication of the PV panel output power under various environmental conditions. / Khalil, Shady; Abu-Rub, Omar H.; Nounou, Hazem.

2018 IEEE Texas Power and Energy Conference, TPEC 2018. Vol. 2018-February Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.

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

Khalil, S, Abu-Rub, OH & Nounou, H 2018, ANN based prognostication of the PV panel output power under various environmental conditions. in 2018 IEEE Texas Power and Energy Conference, TPEC 2018. vol. 2018-February, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2nd IEEE Texas Power and Energy Conference, TPEC 2018, College Station, United States, 8/2/18. https://doi.org/10.1109/TPEC.2018.8312051
Khalil S, Abu-Rub OH, Nounou H. ANN based prognostication of the PV panel output power under various environmental conditions. In 2018 IEEE Texas Power and Energy Conference, TPEC 2018. Vol. 2018-February. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-6 https://doi.org/10.1109/TPEC.2018.8312051
Khalil, Shady ; Abu-Rub, Omar H. ; Nounou, Hazem. / ANN based prognostication of the PV panel output power under various environmental conditions. 2018 IEEE Texas Power and Energy Conference, TPEC 2018. Vol. 2018-February Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6
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