Integrated Energy System Evaluation Based on Singular Value Decomposition and Group Decision Making with Incomplete Information

The transformation of energy development is the inevitable choice to achieve the target of carbon neutrality, and the integrated energy system (IES) is conducive to improving the energy efficiency of users and optimizing energy structure, which is an important direction of energy system transformation in the future. Evaluation of IESs is a major part of the implementation of IES projects, while the current evaluations are based on complete information and single decision making. Given this background, this paper studies the IES evaluation with incomplete information and focuses on the group decision. First, the singular value decomposition algorithm is adopted to predict the missing data. Then, when calculating the index weights, a new method based on standard deviation modified unique reference comparison judgment method is proposed, which not only integrates subjective and objective decision information, but also avoids the problem that the combination coefficients of subjective and objective weights cannot be reasonably allocated. Next, on the basis of the comprehensive weights of indexes and normalized evaluation matrix, the weighted evaluation matrix for each expert is constructed, and the expert weights are calculated by genetic algorithm. Finally, the ranking of IES schemes can be realized by integrating expert weights and expert scoring values. Based on the data of IES at a hospital in Henan, the performance of the proposed method is verified experimentally. Experiments results show that the proposed method is reasonable and effective.