Enhancing short-term wind power forecasting accuracy for reliable and safe integration into power systems: A gray relational analysis and optimized support vector regression machine approach

The reliability and safety of power systems heavily depend on accurate forecasting of new energy generation. However, the non-stationarity and randomness of new energy generation power increase forecasting difficulty. This paper aims to propose a short-term wind power forecasting method with strong characterization ability to accurately understand future new energy generation conditions so as to ensure power systems' reliability and safety. The required input variables for wind power forecasting are determined by the gray relational analysis method. An advanced marine predators algorithm is proposed by improving the marine predators algorithm to enhance convergence ability and probability of escaping local optimal solutions. The advanced marine predators algorithm optimizes support vector regression machine to address the issue of insufficient utilization of its forecasting performance due to the selection of parameter values based on personal experience in traditional methods. Finally, different wind power generation scenarios verify its effectiveness and universality. This study promotes the application of artificial intelligence technology for improving short-term wind power forecasting accuracy, thereby enhancing the reliability and safety level of power systems.