Study on Classification of Electricity Consumption Behavior of Power Users Based on ACO-ELM

• Published in: 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) Vol. 1; pp. 72 - 75
• Main Authors: zhai, guangxin, Chunguang, He, Hua, Shao, Jiakun, An, Jinglin, Han, Shiyao, Hu, Pengfei, Sun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2019
• Subjects: ant colony optimization; big data; extreme learning machine; user electricity behavior
• DOI: 10.1109/IHMSC.2019.00025

Aiming at the classification problem of power users' behavior under the background of big data, a classification method of power users' behavior based on ant colony optimize extreme learning machine (ACO-ELM) was proposed. First, the problem of optimizing the initial input weight and hidden layer neuron threshold of the extreme learning machine is transformed into the problem of ants seeking the best path in the ant colony algorithm. Through the updating, mutation and heredity of ant colony algorithm, the extreme learning machine is trained, and the input weight and threshold with the minimum error are selected to improve its prediction accuracy. ACO-ELM was used to classify power users according to power load data. Experimental simulation verifies that ACO-ELM has better prediction accuracy and accurate classification of power users.