CLUSTERING ANALYSIS OF POWER SYSTEM LOAD SERIES BASED ON ANT COLONY OPTIMIZATION ALGORITHM
Shangwei Liu · Proceedings of the CSEE · 2005
According to the performance of short-term load forecasting(STLF)model based on the principle of artificial neural networks(ANN),the forecasting 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 will also lead to the reduce of forecasting accuracy.To use power load series for characteristic clustering combination with pattern recognition may use as one method of solving the problem.In this paper,the characteristic clustering and its analysis to power load series based on Ant Colony Optimization Algorithm(ACOA) was presented.The load clustering performance of ACOA in actual load system has shown its superiority,which has more sensitivity and resolution to climatic anomaly circumstances,high temperature area,to festival and holiday condition than Kohonen neural network based clustered method,and which has more exquisite and even of the clustering characteristics on the similarity of load curve profile.The above clustering performance has a most important significance to improve the accuracy of STLF.