Ultra-short-Term PV Power Generation Prediction Based on Gated Recurrent Unit Neural Network

Tian, Fengyuan; Fan, Xuexin; Fan, Yaxiang; Wang, Ruitian; Lian, Chuanqiang

doi:10.1007/978-981-19-1532-1_8

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…LSTM-RNN model is used for PV generation forecasting and gives best result comparing to three different methods multiple linear regression (MLR), bagged regression trees (BRT) and basic NN in [7], while in [8] a different structured LSTM networks model is used for hour ahead forecasting for solar power, that achieve good results with reduced computation time. Hybrid model based on modified CNN and Bi-GRU is used for shortterm PV power generation prediction with a good prediction performance is introduced in [9]. CNN-LSTM networks combined model is proposed for solar energy production and achieves performance improvement in comparison to other traditional models [10].…”

Section: Fig 1 Lstm Blockmentioning

confidence: 99%

A hybrid model of CNN and LSTM autoencoder-based short-term PV power generation forecasting

Ibrahim,

Gharghory,

Kamal

2024

Electr Eng

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Solar energy is one of the main renewable energies available to fulfill global clean energy targets. The main issue of solar energy like other renewable energies is its randomness and intermittency which affects power grids stability. As a solution for this issue, energy storage units could be used to store surplus energy and reuse it during low solar generation intervals. Also, in order to sustain stable power grid and better grid operation and energy storage management, photovoltaic (PV) power forecasting is inevitable. In this paper, new hybrid model based on deep learning techniques is proposed to predict short-term PV power generation. The proposed model incorporates convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder network. The new model differentiates itself in accomplishing high prediction accuracy by extracting spatial features in time series via CNN layers and temporal features between the time series data through LSTM. The introduced model is tested on dataset of power generation from southern UK solar farm and the weather data corresponding to same location and time intervals; the forecasting performance of the suggested model is evaluated in metrics of root-mean-square error (RMSE) and mean absolute error (MAE). The used model is compared with different models from the literature either of pure type of network such as LSTM and gated recurrent unit (GRU) or hybrid combination of different networks like CNN-LSTM and CNN-GRU. The results show that proposed model provides enhanced results and reduces training time significantly compared to other competitive models, where the performance of the proposed model improved averagely by 5% to 25% in terms of RMSE and MAE performance metrics, and the execution time of training significantly reduced with almost 70% less compared to other models.

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