Sequential correction method for load resources data based on the minimum enclosing ball clustering

Electricity load data is typically analyzed on a temporal scale, which can vary with seasons or different times of the day. When examined on a spatial scale, electricity load data display distinct regional characteristics. Therefore, ensuring accurate data correction under time-series conditions is paramount. In this paper, we present a sequential correction method for load resource data based on minimum enclosing ball clustering. This method is structured into four phases. In the initial phase, data feature extraction is conducted using load similarity based on a dynamic time planning algorithm to identify the data for processing. Subsequently, the Singular Value Decomposition (SVD) dimensionality reduction algorithm is applied in the second phase to decrease the dimensionality of the data for the subsequent steps. The third phase involves utilizing the minimum enclosing ball clustering technique to cluster and analyze the data obtained from the previous step. Finally, in the fourth phase, the uncorrected values are adjusted.