Energy losses estimation by polynomial fitting and k-means clustering

Abstract

This paper represents an approach for the estimation and forecast of losses in a distribution power grid from data which are normally collected by the grid operator. The proposed approach utilizes the least squares optimization method in order to calculate the coefficients needed for estimation of losses. Besides optimization, a machine learning technique is introduced for clustering of coefficients into several seasons. The amount of data used in calculations is very large due to the fact that electrical energy injected in distribution grid is measured every fifteen minutes. Therefore, this approach is classified as the big data analysis. The used data sets are available in the Serbian distribution grid operator?s report for the year 2017. Obtained results are fairly accurate and can be used for losses classification as well as future losses estimation.