Abstract
High-precision wind power prediction could reference the optimal dispatch and stable operation of the power system. This paper proposes an adaptive hybrid optimization algorithm that integrates decomposition and reconstruction to effectively explore the potential characteristics and related factors of wind power output and improve the accuracy of short-term wind power prediction. First, the extreme-point symmetric mode decomposition is used to analyze the periodicity, trend, and abrupt characteristics in the original wind power sequence and form multiple intrinsic mode functions with local time-domain characteristics. Then, considering the similarity of the feature sequence and the efficiency of the prediction algorithm, the permutation entropy is used to reconstruct the components with close time-domain characteristics to form subsequences that could reflect different spectral characteristics. Then, the improved maximum relevance minimum redundancy-the long short-term memory-the adaptive boosting algorithm model is used to determine the prediction model structure, parameters, and optimal feature factors of the subsequences. Finally, the prediction results of each subsequence are integrated to obtain the final wind power. Taking a wind farm in northern Shaanxi as the application object, the prediction accuracy and efficiency of the methods proposed in this paper are compared in terms of the decomposition method, prediction model, and prediction timeliness. The results show that in the 15 min to 3 h forecast periods, compared with other models, the mean absolute error and root mean square error of the proposed model are increased. At the same time, as the forecast period grows, the superiority of the proposed method is more prominent.
Highlights
As the country pays more attention to clean energy, the proportion of wind power in the power system has gradually increased
This is because each component after decomposition and reconstruction of Extreme-point Symmetric Mode Decomposition (ESMD)-permutation entropy (PE) reflects the change in wind power in different periods to a certain extent and can respond in time when the forecast period grows, which has certain advantages over other decomposition methods
The main conclusions are as follows: (1) An adaptive hybrid optimization model combining decomposition and reconstruction is proposed, which is applied to wind power prediction
Summary
As the country pays more attention to clean energy, the proportion of wind power in the power system has gradually increased. With the rise of deep learning, Long Short-Term Memory (LSTM) is gradually applied to wind power prediction, which solves the problem that traditional neural networks cannot learn long-distance dependencies and improves the accuracy of the forecast. In response to the above problems, this paper proposes a new hybrid short-term wind power forecasting method, which uses ESMD-PE to decompose and reconstruct the original wind power sequence, uses mRMR to select input features for each sub-sequence, and uses LSTM-Adaboost for prediction.
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