Prediction model for cost data of a power transmission and transformation project based on Pearson correlation coefficient–IPSO–ELM

• Published in: Clean energy (Online) 2021-12, Vol.5 (4), p.756-764
• Main Authors: Xin, Ju, ShangKe, Liu, YanLi, Xiao, Ye, Wan
• Format: Article
• Language: English
• ISSN: 2515-4230; 2515-396X
• DOI: 10.1093/ce/zkab052

In view of the difficulty in predicting the cost data of power transmission and transformation projects at present, a method based on Pearson correlation coefficient–improved particle swarm optimization (IPSO)–extreme learning machine (ELM) is proposed. In this paper, the Pearson correlation coefficient is used to screen out the main influencing factors as the input-independent variables of the ELM algorithm and IPSO based on a ladder-structure coding method is used to optimize the number of hidden-layer nodes, input weights and bias values of the ELM. Therefore, the prediction model for the cost data of power transmission and transformation projects based on the Pearson correlation coefficient–IPSO–ELM algorithm is constructed. Through the analysis of calculation examples, it is proved that the prediction accuracy of the proposed method is higher than that of other algorithms, which verifies the effectiveness of the model. Using Pearson correlation coefficients to screen the input variables of the ELM prediction model for the cost data of a power transmission project can simplify the prediction process. Using the IPSO algorithm with ladder-structure coding can determine the parameters for ELM and further improve the prediction accuracy of the cost-prediction model. Graphical Abstract