Digital Grid Investment Risk Prediction and Identification under PSO-FAHP Based Intelligent Algorithm

作者：Yuhong Zhang, Ying Du, Ying Zhou, Hanjing Liu, Ping Zhou, Jie Jiao · 年份：2024 · DOI：10.1145/3674225.3674342 · 被引用次数：1 · 研究领域：Energy Load and Power Forecasting、Risk and Portfolio Optimization、Insurance and Financial Risk Management

Combined with the actual situation of power grid enterprise investment, this paper analyzes the main steps of investment risk management of power grid enterprise, and carries out several researches from risk identification, risk prediction, risk assessment and early warning. First of all, data mining technology was used to analyze the event from the perspective of the whole system, to find out each risk factor affecting the occurrence of the event and classify it, and to construct an index system. Improved gray correlation analysis was used to analyze the influencing factors of power grid investment risk, obtain the indicator evaluation system, analyze the role of the relationship between the influencing factors, and provide ideas for investment decision-making. Secondly, a new grid investment risk prediction method is proposed, combining qualitative and quantitative methods to comprehensively assess the risk of grid investment. The indicators are divided into qualitative and quantitative indicators, and the investment risk prediction library is constructed through a technical method with strong objectivity. A combination model of intelligent algorithms based on particle swarm, support vector machine, and neural network is established to predict the indicators such as economic rationality, and the prediction accuracy of the model is verified, so as to prepare for the subsequent assessment of risks. This report sets three levels of warning for indicators and five levels of war...