A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting

Jie Liu, Quan Shi, Ruilian Han and Juan Yang
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Jie Liu: Center for Energy Environmental Management and Decision-Marking, China University of Geosciences, Wuhan 430074, China
Quan Shi: Center for Energy Environmental Management and Decision-Marking, China University of Geosciences, Wuhan 430074, China
Ruilian Han: Center for Energy Environmental Management and Decision-Marking, China University of Geosciences, Wuhan 430074, China
Juan Yang: Center for Energy Environmental Management and Decision-Marking, China University of Geosciences, Wuhan 430074, China

Energies, 2021, vol. 14, issue 20, 1-22

Abstract: Accurate and timely wind power forecasting is essential for achieving large-scale wind power grid integration and ensuring the safe and stable operation of the power system. For overcoming the inaccuracy of wind power forecasting caused by randomness and volatility, this study proposes a hybrid convolutional neural network (CNN) model (GA–PSO–CNN) integrating genetic algorithm (GA) and a particle swarm optimization (PSO). The model can establish feature maps between factors affecting wind power such as wind speed, wind direction, and temperature. Moreover, a mix-encoding GA–PSO algorithm is introduced to optimize the network hyperparameters and weights collaboratively, which solves the problem of subjective determination of the optimal network in the CNN and effectively prevents local optimization in the training process. The prediction effectiveness of the proposed model is verified using data from a wind farm in Ningxia, China. The results show that the MAE, MSE, and MAPE of the proposed GA–PSO–CNN model decreased by 1.13–9.55%, 0.46–7.98%, and 3.28–19.29%, respectively, in different seasons, compared with Single–CNN, PSO–CNN, ISSO–CNN, and CHACNN models. The convolution kernel size and number in each convolution layer were reduced by 5–18.4% in the GA–PSO–CNN model.

Keywords: convolutional neural network; ultra-short-term; wind power forecasting; hybrid; genetic algorithm; particle swarm optimization (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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