An advanced weighted system based on swarm intelligence optimization for wind speed prediction

High precision wind speed forecasting will maximize the utilization of wind power, which is essential for wind farm operation and energy system management. But the inherent instability and volatility of wind speed bring difficulties in the forecasting and operation processes. At present, experts and scholars have proposed many wind speed prediction methods. However, parts of studies ignored the importance of parameter optimization and data preprocessing, which made the results vulnerable to the instability of a single model. To fill this gap, a weighted combination model is obtained by an advanced swarm intelligence optimization algorithm to overcome limitations of the individual neural network. At the same time the denoising technology is implemented to reduce the noise in original speed sequences. Our empirical study and multi-angle evaluation results show that the advanced optimization algorithm we adopted is superior to other well-known meta-heuristic algorithms. And the experimental results show that the novel system owns strong stability and high forecasting accuracy. It can not only provide a new idea for the field of wind power forecasting, but also open an effective way for smart planning in the future.