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Air pollution has received more attention from many countries and scientists due to its high threat to human health. However, air pollution prediction remains a challenging task because of its nonstationarity, randomness, and nonlinearity. In this research, a novel hybrid system is successfully developed for PM2.5 concentration prediction and its application in health effects and economic loss assessment...
With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed. This paper proposes a combination model for probabilistic short-term wind speed forecasting. In this proposed hybrid approach, EWT (Empirical...
Wind power, as a promising renewable energy source, has environmental benefits, as well as economic and social ones. To evaluate wind energy properly and efficiently, this study proposes a hybrid forecasting approach that combines the Extreme Learning Machine (ELM), which rarely presents in literature on wind speed forecasting, the Ljung-Box Q-test (LBQ) and the Seasonal Auto-Regressive Integrated...
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