Photovoltaic power prediction based on dilated causal convolutional network and stacked LSTM

Chongyi Tian; Longlong Lin; Yi Yan; Ruiqi Wang; Fan Wang; Qingqing Chi; Chongyi Tian; Longlong Lin; Yi Yan; Ruiqi Wang; Fan Wang; Qingqing Chi

doi:10.3934/mbe.2024049

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 1: 1167-1185. doi: 10.3934/mbe.2024049

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Research article Special Issues

Photovoltaic power prediction based on dilated causal convolutional network and stacked LSTM

1.
Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
2.
State Grid Shandong Integrated Energy Services CO., LTD, Jinan 250101, China

Academic Editor: Shangce Gao

Received: 08 September 2023 Revised: 12 November 2023 Accepted: 27 November 2023 Published: 25 December 2023

Due to the crucial role of photovoltaic power prediction in the integration, scheduling and operation of intelligent grid systems, the accuracy of prediction has garnered increasing attention from both the research and industry sectors. Addressing the challenges posed by the nonlinearity and inherent unpredictability of photovoltaic (PV) power generation sequences, this paper introduced a novel PV prediction model known as the dilated causal convolutional network and stacked long short-term memory (DSLSTM). The methodology begins by incorporating physical constraints to mitigate the limitations associated with machine learning algorithms, thereby ensuring that the predictions remain within reasonable bounds. Subsequently, a dilated causal convolutional network is employed to extract salient features from historical PV power generation data. Finally, the model adopts a stacked network structure to effectively enhance the prediction accuracy of the LSTM component. To validate the efficacy of the proposed model, comprehensive experiments were conducted using a real PV power generation dataset. These experiments involved comparing the predictive performance of the DSLSTM model against several popular existing models, including multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), stacked LSTM and stacked GRU. Evaluation was performed using four key performance metrics: Mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R2). The empirical results demonstrate that the DSLSTM model outperforms other models in terms of both prediction accuracy and stability.
- photovoltaic (PV) prediction,
- dilated causal convolution network (DCCN),
- stacked LSTM network,
- deep learning,
- hybrid deep learning
Citation: Chongyi Tian, Longlong Lin, Yi Yan, Ruiqi Wang, Fan Wang, Qingqing Chi. Photovoltaic power prediction based on dilated causal convolutional network and stacked LSTM[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 1167-1185. doi: 10.3934/mbe.2024049

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Abstract

Due to the crucial role of photovoltaic power prediction in the integration, scheduling and operation of intelligent grid systems, the accuracy of prediction has garnered increasing attention from both the research and industry sectors. Addressing the challenges posed by the nonlinearity and inherent unpredictability of photovoltaic (PV) power generation sequences, this paper introduced a novel PV prediction model known as the dilated causal convolutional network and stacked long short-term memory (DSLSTM). The methodology begins by incorporating physical constraints to mitigate the limitations associated with machine learning algorithms, thereby ensuring that the predictions remain within reasonable bounds. Subsequently, a dilated causal convolutional network is employed to extract salient features from historical PV power generation data. Finally, the model adopts a stacked network structure to effectively enhance the prediction accuracy of the LSTM component. To validate the efficacy of the proposed model, comprehensive experiments were conducted using a real PV power generation dataset. These experiments involved comparing the predictive performance of the DSLSTM model against several popular existing models, including multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), stacked LSTM and stacked GRU. Evaluation was performed using four key performance metrics: Mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R2). The empirical results demonstrate that the DSLSTM model outperforms other models in terms of both prediction accuracy and stability.

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