Wind speed forecasting is a crucial issue in the wind power industry. However, the disadvantage of the existing wind speed forecasting models is that they often ignore similar fluctuation information between the adjacent WTGs (wind turbine generators), which leads to poor forecasting accuracy. This paper proposes a hybrid wind speed forecasting model to overcome this disadvantage. Specifically, grey correlation analysis is applied to select useful fluctuation information from the adjacent and observed WTGs, and the chosen fluctuation information is fed into the v-SVM (v-support vector machine), which offers good capability in nonlinear fitting, to perform wind speed forecasting of the observed WTGs. Meanwhile, to reduce the impacts of the model parameters on the final forecasting performance, CS (cuckoo search) is used to tune the parameters in the v-SVM. The results from two case studies show that the proposed model, which considers the fluctuation information of the adjacent WTG, offers greater accuracy than the other compared models. As concluded from the results of three accuracy tests, the performances of v-SVM and ε-SVM (ε-support vector machine) show no significant difference, and the CS algorithm is more efficient than the PSO (particle swarm optimization) for tuning of the parameters in the v-SVM. •The proposed hybrid model takes advantage of the fluctuation information of the adjacent WTGs.•The relational degree between all inputs and the target is analyzed by grey relation analysis.•Predictive accuracy tests show that CS is more efficient than PSO in optimizing parameters in v-SVM.•The performances of v-SVM and ε-SVM produce no obvious differences in short-term wind speed forecasting in our study.•The hybrid model can improve forecasting accuracy for single WTG.