摘要
基于对电力公司历史发电及排放数据(二氧化硫、氮氧化物和烟尘)的分析,文中首先采用递归神经网络(RNN)拟合出发电功率与排放数据回归模型,然后在该回归模型与蚁群算法(ACO)的基础上设计实时发电调度算法,在满足实时发电任务的前提下使机组总污染物排放量降低,达到节能减排的目的.文章最后通过安徽省电力的真实数据构建仿真实验验证回归模型和调度方案的有效性.
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.
出处
《信息与控制》
CSCD
北大核心
2017年第4期415-421,共7页
Information and Control
基金
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)
关键词
发电调度
节能减排
回归分析
蚁群优化算法
powergeneration dispatching
energy saving and emission reduction
regression analysis
ant colony optimization algorithm
