| Popis: |
In the grid, the forecast of renewable energy has become very important, which is very important for the development of control strategies for the grid. In the grid, the power generation is planned according to the load curve. System operators should incorporate wind energy into economic plans, unit commitments, and reserve allocation issues. Essentially, a neural network is designed to solve short-term prediction problems because it can use non-statistical methods to check the nonlinear relationship between input and output and does not require a pre-defined mathematical model. A macromodel of wind turbine using support vector machine using radial basis function kernel function is developed to estimate the wind speed more efficiently and accurately. This will help to get a forecast model without numerical weather prediction parameters as a forecast input that can be offered in the electricity markets. The preliminary results show that although the development of models and methods is continuous, the current decisions in all fields of wind energy are based on artificial intelligence and machine learning models. This method is developed in the MATLAB environment. |
