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Accurate photovoltaic power forecasting models using deep LSTM-RNN

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Abstract

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.

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Authors and Affiliations

  1. Department of Electrical Engineering, Aswan University, Aswan, 81542, Egypt

    Mohamed Abdel-Nasser & Karar Mahmoud

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  1. Mohamed Abdel-Nasser
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  2. Karar Mahmoud
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Correspondence to Karar Mahmoud.

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Abdel-Nasser, M., Mahmoud, K. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput & Applic 31, 2727–2740 (2019). https://doi.org/10.1007/s00521-017-3225-z

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  • DOI: https://doi.org/10.1007/s00521-017-3225-z

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