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Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods

Zhang, Yachao, Liu, Kaipei, Qin, Liang, An, Xueli
Energy conversion and management 2016 v.112 pp. 208-219
artificial intelligence, clean energy, energy, entropy, models, power generation, prediction, risk, wind power, wind turbines, China
Due to the increasingly significant energy crisis nowadays, the exploitation and utilization of new clean energy gains more and more attention. As an important category of renewable energy, wind power generation has become the most rapidly growing renewable energy in China. However, the intermittency and volatility of wind power has restricted the large-scale integration of wind turbines into power systems. High-precision wind power forecasting is an effective measure to alleviate the negative influence of wind power generation on the power systems. In this paper, a novel combined model is proposed to improve the prediction performance for the short-term wind power forecasting. Variational mode decomposition is firstly adopted to handle the instability of the raw wind power series, and the subseries can be reconstructed by measuring sample entropy of the decomposed modes. Then the base models can be established for each subseries respectively. On this basis, the combined model is developed based on the optimal virtual prediction scheme, the weight matrix of which is dynamically adjusted by a self-adaptive multi-strategy differential evolution algorithm. Besides, a probabilistic interval prediction model based on quantile regression averaging and variational mode decomposition-based hybrid models is presented to quantify the potential risks of the wind power series. The simulation results indicate that: (1) the normalized mean absolute errors of the proposed combined model from one-step to three-step forecasting are 4.34%, 6.49% and 7.76%, respectively, which are much lower than those of the base models and the hybrid models based on the signal decomposition techniques; (2) the interval forecasting model proposed can provide reliable and excellent prediction results for a certain expectation probability, which is an effective and reliable tool for the short-term wind power probabilistic interval prediction.