PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México

Mario Tovar, Miguel Robles and Felipe Rashid
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Mario Tovar: Energy Systems Department, Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Priv. Xochicalco S/N Temixco, Morelos 62580, Mexico
Miguel Robles: Energy Systems Department, Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Priv. Xochicalco S/N Temixco, Morelos 62580, Mexico
Felipe Rashid: Energy Systems Department, Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Priv. Xochicalco S/N Temixco, Morelos 62580, Mexico

Energies, 2020, vol. 13, issue 24, 1-15

Abstract: Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the Lasso regression or the Ridge regression.

Keywords: Neural Networks; CNN; LSTM; PV power predictions; Microgrids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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