We herein report an efficient and green aerobic radical cascade reaction of o-vinylphenylisocyanides with thiols to access a broad scope of 2-thio-substituted quinolines with no need of catalyst or additional...
In recent years, global warming caused by the massive emission of greenhouse gases has brought a series of negative impacts on the living environment of human beings, which is an urgent problem for human society [1]. China's CO 2 emissions are currently ranked first in the world. In particular, the power industry's CO 2 emissions account for more than 40% of the total national emissions [2]. One of the main ways for the power sector to achieve carbon emission reduction targets is to prioritize the development of renewable energy and reduce the use of fossil fuels in generation expansion planning [3][4][5][6]. As a consequence, generation expansion planning plays a key role for the achievement of more renewable energy and low CO 2 emissions objectives in an efficient and effective way.
One of the most important preconditions for guaranteeing a smooth link between wind farms and the power system is to develop an accurate model for forecasting the wind speed. This paper describes a novel wind speed prediction model based on dynamic adaptive variable-weight optimization theory that considers the relevance of historical observations. The model applies signal preprocessing to recorded wind speed observations using ensemble empirical mode decomposition. The decomposed signals are then subjected to a random noise reduction procedure, which improves the robustness of the prediction model. An autoregressive integrated moving average model, general regression neural network, and long short-term memory are used to recognize the different features of each decomposed subsequence. Brain storm optimization is then applied to further promote the forecasting performance by integrating different forecasting models with dynamically adapted variable weights. To evaluate the prediction capacity of the proposed method, three case studies are conducted. The experimental outcomes reveal that the method presented in this paper provides more satisfactory prediction ability and robustness than other models.
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