Accurate photovoltaic power forecasting models using deep LSTM-RNN

Mohamed Abdel-Nasser, Karar Mahmoud*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

464 Citations (Scopus)


Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems. The LSTM networks can model the temporal changes in PV output power because of their recurrent architecture and memory units. The proposed method is evaluated using hourly datasets of different sites for a year. We compare the proposed method with three PV forecasting methods. The use of LSTM offers a further reduction in the forecasting error compared with the other methods. The proposed forecasting method can be a helpful tool for planning and controlling smart grids.

Original languageEnglish
Article number7
Pages (from-to)2727–2740
Number of pages14
JournalNeural Computing and Applications
Publication statusPublished - Jul 2019
MoE publication typeA1 Journal article-refereed


  • Deep learning
  • PV power forecasting
  • Renewable energy sources
  • Smart grids


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