Abstract: In response to the large number of low-voltage residential users and the high cost of installing additional monitoring equipment or upgrading existing monitoring equipment, an improved K-means++ and long short-term memory algorithm based on advanced measurement system for large-scale minute level data collection is proposed for remote load decomposition of residents. Firstly, in order to capture changes in data more accurately and efficiently, and in real-time a sliding window based bilateral accumulation and algorithm monitoring is designed. Secondly, in order to identify representative loads and ensure computation speed, an improved K-means++ algorithm is adopted. Finally, using long short-term memory algorithms, data that undergoes regular changes over time are captured to complete load decomposition. At a low sampling frequency of 1 minute, the applicability of the proposed algorithm is fully validated through the collection of daily load data from residents.

Key words: advanced measurement system, K-Means++ algorithm, LSTM algorithm, residential load, remote decomposition