Hongchao Zuo

TL;DR: A novel hybrid forecasting system is proposed in this paper that includes effective data decomposition techniques, recurrent neural network prediction algorithms and error decomposition correction methods, and decomposes the error to correct the previously predicted wind speed.

TL;DR: Wang et al. as discussed by the authors proposed a hybrid decomposition method coupling the ensemble patch transform (EPT) and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), where EPT is utilized to extract the trend of wind speed, then CEEMDan is employed to divide the volatility into several fluctuation components with different frequency characteristics.

TL;DR: An improved electricity price forecasting model is developed that offers the advantages of adaptive data preprocessing, advanced optimization method and kernel-based model, and optimal model selection strategy, and a newly proposed optimal models selection strategy is applied to determine the developed model that provides the most desirable forecasting result.

TL;DR: The forecasting results show the superior performance of the proposed nonlinear relationship extraction (NRE) method compared to several outstanding forecasters.

TL;DR: Optized ANNs are proposed for sizing and simulating a clean energy community (CEC) that employs a PV-wind hybrid system, coupled with energy storage systems and electric vehicle charging stations, to meet the building district energy demand.