Research on Risk Prediction Model of Power Grid Marketing Project Based on Fuzzy Logic Regression

This study proposes an innovative risk prediction model for power grid marketing, aiming to more comprehensively analyze and evaluate the risk situation under the influence of multiple factors in the marketing process. First, the model uses the fuzzy logic system to construct a multi-dimensional factor evaluation system, and analyzes based on historical data to extract the key influencing factors in the power grid marketing process. These influencing factors not only include the volatility of electricity market prices and load demand, but also cover seasonal changes, customer demand types, and policy orientations. Subsequently, the regression analysis method is used to process and analyze the above key influencing factors and establish a mathematical model for predicting risk. The mathematical model represents the influence of different influencing factors on risk in the form of regression equations, and improves the accuracy of prediction to a certain extent. To verify the effectiveness and applicability of the model, this study simulates in the MATLAB environment, and evaluates the performance of the model in different scenarios through simulation analysis of multiple real power grid marketing cases. The simulation results show that the risk prediction model based on the combination of fuzzy logic and regression analysis constructed in this study has high prediction accuracy in practical applications, with an accuracy rate of more than 85%.