DOI: 10.1002/adc2.161 ISSN:

Short‐term wind power prediction based on the combination of firefly optimization and LSTM

Rui Zhang, Xiu Zheng
  • Modeling and Simulation
  • Control and Systems Engineering
  • Energy (miscellaneous)
  • Signal Processing
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


With the development of social resources, people's consumption of energy is huge, so renewable energy, such as wind energy, has been widely concerned and developed. Although there has been sufficient development of wind power generation, its output has some problems such as uncertainty, which leads to insufficient utilization of wind energy resources and uneven power output quality level, which brings great challenges to the grid connection. To solve this problem, a short‐term wind power prediction model combining firefly algorithm and long term memory network is proposed. The main motivation of the research is to improve the accuracy of wind power prediction and thus improve the utilization of wind energy resources. Compared with the existing methods, the innovation of FA‐LSTM model lies in the integration of the two algorithms, making full use of the advantages of FA in global search optimization and LSTM in time series data processing, and improving the accuracy and stability of prediction. During the experiment, we used different wind farm data to train and test the model. The results show that the FA‐LSTM model can improve the optimal fitness by more than 50% compared with other algorithms, and the iterative prediction error is smaller. Standard root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the model. The accuracy of RMSE and MAE reached over 97% and 98% respectively. When the test data is highly volatile, the data accuracy of FA‐LSTM model reaches 92% and 94%, and the FA‐LSTM model drops to the stable value faster. FA‐LSTM model has the best fitting degree with the true value curve, and the fitting degree reaches more than 90%. Comparing the actual power and predicted power of different units, the actual power of Unit 1 is 34.875, and the predicted power obtained by FA‐LSTM model is 34.935, with an error of only 0.06. The key finding of this study is that the prediction model combining FA and LSTM has high accuracy and stability in wind power prediction, and can effectively deal with the uncertainty and volatility of wind energy resource utilization. FA‐LSTM model provides an effective solution for wind power prediction, which is helpful to improve the utilization rate of wind energy resources.

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