Load Data Analysis Based on Timestamp-Based Self-Adaptive Evolutionary Clustering

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 12; pp. 1 - 10
• Main Authors: Lin, Rongheng, He, Zheyu, Zou, Hua, Wu, Budan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Algorithms; Behavior pattern; Behavioral sciences; Classification algorithms; Clustering; Clustering algorithms; Clustering methods; Data analysis; Data mining; Evolutionary algorithms; evolutionary clustering; Forecasting; Heuristic methods; Informatics; load profile; Run time (computers); Smart grid; Total quality; User behavior
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2023.3247010

Smart grid system can obtain users' daily load data, and by clustering, we can get users' load profiles to divide them into industrial, commercial and residential types. Load data has the characteristic of changing periodically. Within a period, the load profiles are relatively stable. However, load profiles often changes significantly according to reasons like holidays and season changing. When conducting a continuous clustering task for consecutive days, traditional clustering algorithms cannot consider the time-dimension features into analysis, which may make clustering results be very different even if user behaviors are almost the same. Evolutionary clustering (EC) can be taken into consideration. EC doesn't ignore historical clustering results and makes results more stable in a period. However, when dramatic changes happen in user behaviors, the quality of EC's clustering results will decrease significantly. This paper proposed an optimized evolutionary clustering algorithm: Timestamp-Based Self-Adaptive Evolutionary Clustering (TBSAEC). TBSAEC is based on evolutionary clustering, and takes a heuristic approach to pick the evolutionary parameter to maximize the total quality for one certain timestamp. TBSAEC maintains the stability of continuous-time clustering results while better adapting to changes in user behaviors. Besides, TBSAEC optimize the running efficiency of the algorithm by picking samples in equal portions from historical data instead of the whole data. We applied TBSAEC to the load data of a certain region in east China in 2015, and the results showed that TBSAEC is 3% to 9% higher than the ordinary evolutionary clustering algorithm in total quality, and 87% faster than the ordinary algorithm. Applying TBSAEC to power grid systems can provide more precise users' load profiles, which makes them create more accurate and profitable power plans.