Deterministic and probabilistic multi-step forecasting for short-term wind speed based on secondary decomposition and a deep learning method

Abstract The stochastic and intermittent nature of wind speed brings rigorous challenges to the safe and stable operation of power system. Wind speed forecasting is crucial for availably dispatching the wind power resource. In this paper the proposed model based on secondary decomposition (SD) and bidirectional gated recurrent unit (BiGRU) can accommodate long-range dependency and extract the semantic information of raw data. In the model, the GRU method is improved in directional nature. A second layer is added in GRU network to connect the two reverse and separate hidden layers to the same output layer. The PSR-BiGRU model of each subsequence is established and chicken swarm optimization (CSO) algorithm is employed to jointly optimize the parameters. The proposed method focuses on deterministic and probabilistic forecasting and does not involve any distribution assumption of the prediction errors needed in most existing forecasting methods. The effectiveness and advancement of the proposed model is tested by using data from two different wind farms. Comparing with other hybrid models, the proposed hybrid model is suitable for wind speed forecasting and could obtain better forecasting performance.