A fast load pattern extraction approach based on dimension reduction and sampling

This paper proposes a fast load pattern extraction approach to solve the time consuming problem in using a traditional k-means clustering method for large volumes of load curves. The approach, based on dimension reduction and sampling, segments and averages sampling characteristic points to reduce the load curve's dimensions, then reduces the overall size of the sample data set using representative random sampling. K-means clustering algorithm is used to extract load patterns from the representative data set, which will be used to classify the full data set. Reducing the size and dimension of the data set allows use of a less complex algorithm, and thus greatly improves the clustering speed. The validity of the approach is proven by experiments designed to evaluate the trade-off between complexity and consistency.