Integrated energy system evaluation method based on dimensionality reduction and indexes updating with incomplete information.

The integrated energy system (IES) can improve energy efficiency and promote the use of renewable energy. It is of great significance to find the optimal alternative from many IES alternatives. However, most studies focused on the IES evaluation with complete information. Given this background, this paper proposes an evaluation method based on generalized regression neural network (GRNN) and probabilistic neural network (PNN) to address the issue of IES evaluation with incomplete information, which takes the running speed of the algorithm and the dynamic updating of the indexes into account. Firstly, the missing data is predicted via GRNN, and a complete evaluation matrix is obtained. Then, the evaluation indexes are clustered by affinity propagation algorithm, and the evaluation matrix of the cluster centers is determined, which can reduce the dimension of the initial matrix and improve the speed of the IES evaluation method. For the newly added evaluation indexes, the PNN is used to classify them, and the new cluster center is calculated, which can address the issue of the indexes updating. Based on the data of a hospital in Henan, case studies were carried out to evaluate the performance of the proposed method. The results show that the proposed method performs better than the existing IES evaluation method. • GRNN is used to address the IES evaluation problem with incomplete information. • The accuracy of GRNN is verified by comparing with common prediction methods. • It is the first work to deal with the issue of indexes updating in IES evaluation. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]