Short-term wind power prediction based on GPR-BSO model

Wind power forecasting is a crucial part for the safe and stable operation of wind power integration, which is under the influence of different factors such as wind speed, wind direction, atmospheric pressure. These factors bring randomness and volatility to wind power which makes it less predictable. While, there are very limited studies on describing the uncertainty of wind power. Therefore, to providing additional information on the uncertainty and volatility, a kernel-based on Gaussian Process Regression (GPR) incorporating the hyper-parameters intelligent optimization method is proposed in this paper. Firstly, the hyper-parameters solution of GPR is formulated as a nonlinear optimization with constraints. Then, an intelligent algorithm named Brain-storming optimization (BSO) is adopted to obtain the optimal hyper-parameters of GPR. Furthermore, the performance is examined on short-term wind power data. Most importantly, the GPR incorporating BSO can avoid the hyper-parameters at local optimum.