Zhenkun Liu

TL;DR: A forecasting system is developed based on a data pretreatment strategy, a modified multi-objective optimization algorithm, and several forecasting models that positively exceeds all contrastive models in respect to forecasting precision and stability.

TL;DR: The experimental results reveal that the proposed combined forecasting system can provide effective wind speed point and interval forecasts and is deemed more useful for the scheduling and management of electric power systems than other benchmark models.

TL;DR: Wang et al. as discussed by the authors proposed an ensemble forecasting system that integrates data decomposition technology, sub-model selection, a novel multi-objective version of the Mayfly algorithm, and different predictors to better demonstrate the stochasticity and fluctuation of wind speed data.

TL;DR: In this paper, a health-related economic loss evaluation system is proposed, which deals with PM2.5 distribution, optimization of distribution parameters, and evaluation of healthrelated economic losses.

TL;DR: A novel ensemble forecasting system is proposed by integrating the decomposition strategy, sub-model selection, and ensemble point and interval prediction based on the newly proposed multi-objective Archimedes optimization algorithm, which has been demonstrated to be effective at the theoretical and empirical levels for providing reliable wind speed forecasting results.