Power System Short-term Load Forecasting Based on Neural Network with Artificial Immune Algorithm

摘要：

This paper offers one kind of improved artificial immune algorithm which takes different mutation strategy toward different unit that has various quality. This algorithm conducts self-adapt adjustment between mutation rate and crossover rate in order to achieve balance between search accuracy and search efficiency. This paper conducts DAIA-BPNN short-term power load forecast model based on DAIA algorithm. It uses DAIA algorithm to optimize the weight and threshold of BPNN while overcoming the blindness when selecting the weight and threshold of BPNN. The actual calculation example of the short-term power system load forecast shows that the method presented in this paper has higher forecast accuracy and robustness compared with artificial neural networks and regression analysis model.

关键词： artificial immune algorithm neural network power system load forecasting

作者: Huang Yue Li Dan Gao Liqun

作者单位: School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110168, China Northeast China Grid Company Limited, Shenyang 110180, China School of Information Science and Engineering, Northeastern University, Shenyang 110004, China

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 844-848

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）