The Application of an Improved Ant Colony Clustering in the Power Load Forecasting
Ant colony algorithms have been recently suggested for short-term load forecasting (STLF) by a large number of researchers. As we know that the forecast accuracy is influenced by the distributed feature of load sample space, and the complex nonlinear relation, which is formed by the sensibility of external weather factors to power load, so an Improved Ant Colony Clustering (IACC) based on Ant Colony Algorithm was put forward. In IACC, each load data was represented by an ant, and the merits of IACC were parallel search optimum and the dynamic method to adjust the evaporation coefficient, which can raise the forecast accuracy. What’s more, IACC was more sensitive to weather condition and date than the Ant Colony Optimization Algorithm (ACOA). The comparison between IACC and ACOA shows that IACC increased the STLF accuracy, and IACC is more exquisite to the similarity of load curve profile.
Power Load Forecasting ACOA IACC Colony Clustering Load Curves
Li Wei Han Zhuhua
Department of Business and Administration, North China Electric Power University, Baoding, China
国际会议
北京
英文
2007-11-01（万方平台首次上网日期，不代表论文的发表时间）