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《Information and Control》 2017-04
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Power Generation Dispatching for Environmental Protection Based on Recursive Neural Network and Ant Colony Optimization Algorithm

KE Yuyang;YANG Xunzheng;XIONG Yan;LIANG Xiao;School of Computer Science and Technology,University of Science and Technology of China;Dispatching and Control Center,Anhui Electric Power Corporation;  
We propose a new regression model to fit power generation and emission data( SO2,NOx,and soot) by using recurrent neural network( RNN). On the basis of the regression model and ant colony optimization( ACO),we design a real-time power generation dispatching algorithm for reducing total pollutant emissions under the premise of completing real-time power generation and achieving energy saving and emission reduction. We evaluate our proposal by using the real electricity data of Anhui Electric Power. Experimental results show the effectiveness of our method.
【Fund】: 国家自然科学基金重点资助项目(61232018);; 国网安徽省电力公司科技项目(52120015007W)
【CateGory Index】: TM73;TP18
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