A Trusted Measurement Model of AI Algorithm for Power Grid Scheduling in Predictive Scenarios

With the continuous expansion of the power grid and the rapid development of renewable energy, power grid dispatching is facing increasingly complex challenges. To improve the efficiency and reliability of grid dispatching, AI technology has been widely applied to load forecasting and optimized scheduling. However, the application of AI algorithms in grid dispatching also brings new issues, especially regarding the trustworthiness of these algorithms. If the predictions made by AI algorithms are not accurate enough or errors occur under certain conditions, it will directly affect the safety and stability of the grid. Therefore, establishing a model that can effectively evaluate the trustworthiness of AI algorithms in grid dispatching is crucial. This paper utilizes fuzzy theory to construct a trustworthiness evaluation model for predictive AI algorithms, which can calculate the overall trustworthiness of the algorithm, ensuring that its application in grid dispatching maintains a high level of trustworthiness and reliability.