Affiliations: [a] State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing, China
| [b]
School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China
Correspondence:
[*]
Corresponding author. Shuang Bai, School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, China. E-mail: [email protected].
Abstract: Accurately evaluating the technological improvement effects of wind turbines is crucial for wind farm operators. To this end, this paper proposes an innovative approach that employs a wind power regression model which leverages external environmental information to predict the output power of wind turbines. The effectiveness of technological improvements can be evaluated by comparing the predicted output power with the measured output power. In this paper, a model called stacked LSTM networks with attention mechanisms is designed. In the proposed model, the stacked LSTM networks are used to enhance the nonlinear fitting ability and capture deeper features of the input sequence. Furthermore, temporal attention mechanisms are employed to make the model focus on important time-series information of the data. In addition, a hierarchical attention mechanism is designed to explore the correlation among the outputs of the stacked LSTM networks and enrich the model’s output information. The experiments on the data from a wind farm show that the proposed method outperforms various wind power prediction benchmarks, achieving lower RMSE, MAE, and MAPE values of 142.82, 104.2, and 4.85%, respectively.
Keywords: Wind power regression prediction, evaluation of technological improvement effect, stacked LSTMs, temporal attention mechanism, hierarchical attention mechanism
