A semantic clustering method for selecting the typical day load curve
Sun Zheng · Journal of North China Electric Power University · 2013
A method of transforming the typical day load curve selection problem into the multiple classification problems based on statistical learning is proposed.The Probabilistic Latent Semantic Analysis(PLSA) is used to solve the problem.Firstly,observed characteristic words and target documents are formed by K mean clustering and load curves' division,and the characteristic words-target co-occurrence matrix is obtained based on threshold calculation;secondly,based on the Davies-Bouldin index,the best topic number of PLSA model is calculated,and the model's parameters are solved to get the potential topic of each characteristic word in target documents;finally,on the basis of the correspondence between the load curves and characteristic words,new clusters are formed,then the typical days of each cluster are selected by using strategies.The experiment shows that this method can better reflect the factor effect,such as holidays,climate and other factors,and the typical daily selection is reasonable and feasible.