Decision Technique of Solar Radiation Prediction Applying Recurrent Neural Network for Short-Term Ahead Power Output of Photovoltaic System

  • Yona A
  • Senjyu T
  • Funabashi T
  • et al.
N/ACitations
Citations of this article
14Readers
Mendeley users who have this article in their library.

Abstract

In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other method

Cite

CITATION STYLE

APA

Yona, A., Senjyu, T., Funabashi, T., Mandal, P., & Kim, C.-H. (2013). Decision Technique of Solar Radiation Prediction Applying Recurrent Neural Network for Short-Term Ahead Power Output of Photovoltaic System. Smart Grid and Renewable Energy, 04(06), 32–38. https://doi.org/10.4236/sgre.2013.46a004

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free