Cloud Model-Based Improved Evidence Theory and Its Applications to Power Systems

In order to cope with the complex features of ambiguity, randomness and uncertainty in multi-attribute decision-making problems, this paper introduces the Dempster-Shafer evidence theory in the framework of cloud modeling. First, a cloud model is used to calculate the affiliation of each evaluation metric, which was subsequently converted to a basic confidence assignment function. Second, the game theory idea is borrowed to combine the dynamic and static weights of the evidence in the game, to improve the traditional evidence theory and realize the effective integration of information. The idea of average fit is identified again, and a comprehensive evaluation conclusion is drawn by comparing the closeness of the evaluation object to the optimal and worst solutions. The new electric power system investment project is illustrated, and the applicability of the algorithm is verified.