Yanbin Yuan

TL;DR: Compared with the Back Propagation neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction.

TL;DR: In this article, an improved particle swarm optimization (IPSO) method was proposed to solve the dynamic load economic dispatch problem (DLED) with valve-point effects, where feasibility-based rules and heuristic strategies with priority list based on probability are devised to handle constraints effectively.

TL;DR: In this article, the accuracy of hybrid long short-term memory neural network and ant lion optimizer model (LSTM-ALO) in prediction of monthly runoff was investigated.

TL;DR: In this paper, a hybrid autoregressive fractionally integrated moving average and least square support vector machine model is proposed to forecast short-term wind power, which takes advantage of the respective superiority of autoregression fractionally integrating moving average (AFIMA) and the least square SVM (LSVM).

TL;DR: In this article, the authors integrated wind power with hydrothermal scheduling to establish multi-objective economic emission hydro-thermal-wind scheduling problem (MO-HTWS) model with considering wind uncertain cost.