Residential Electricity Consumption Behavior Mining Based on System Cluster and Grey Relational Degree
- 1 School of Electrical Engineering and Information, Sichuan University, Chengdu, China
- 2 School of Electrical Engineering and Information, Sichuan University, Chengdu, China
Abstract
In order to improve the utilization of the residential electricity consumption data which contains the information on the user’s electricity consumption habits, a residential electricity consumption behaviors mining algorithm model is constructed. Firstly, according to the attribute, the collected data can be divided into the global data and the phase data, then the appropriate global variables are selected to mine the user’s electricity consumption patterns in the near future on the system clustering algorithm. Based on the theory of grey relational analysis, combing phase data with the power modes to analyze the potential characteristics of residential electricity consumption behaviors deeply that verify the ability of latest power mode to predict household electricity consumption situation in the coming few days and the effect of dominant phase variables on the peak load shifting. Finally, from the actual data of a certain family, the proposed data mining algorithm is testified that it can effectively explore the electricity consumption behavior habits and characteristics of the family.
- Sun, G.Q., Li, Y.C., Wei, Z.N., Yang Y.B., Zang, H.X. andBian, D. (2015) Discussion on Interactive Architecture of Smart Power Utilization. Automation of Electric Power System, 39, 68-74.
- Li, Y., Wang, B.B. andLi, F.X. (2015) Outlook and Thinking of Flexible and Interactive Utilization of Intelligent Power. Automation of Electric Power System, 39, 2-9.
- Wang, G.H. (2012) Practice and Prospect of China Intelligent power Utilization. Electric Power, 45, 1-5.
- Lin, H.Y., Zhang, J., Xu, K.P., Pi, X.J. (2012) Design of Interactive Service Platform for Smart Power Consumption. Power System Technology, 36, 255-259.
- He, Y.X., Wang, B.Xiong, W., Zhang, T. and Liu, Y.Y. (2012) Analysis of Residents’ Smart Electricity Consumption Behavior Based on Fuzzy Synthetic Evaluation and the Design of Interactive Mechanism. Power System Technology, 36, 247-252.
- Sheng, W.X., Shi, C.K., Sun, J.P., Zhang, B. and Zhang, T.S. (2013)Characteristics and Research Framework of Automated Demand Response in Smart Utilization. Automation of Electric Power System, 37, 1-7.
- Yin, S.G., Zhang, Y., Bai, K.M. (2009) A Smart Power Utilization System Based on Real-Time Electricity Prices. Power System Technology, 33, 11-16.
- Zhang, X., Li, D.H. andCheng, M. (2015) Study on Peak Load Shifting Management Based on the Big Data Technology. Modern Electric Power, 32, 66-70.
- Zhao, L., Hou, X.Z., Hu, J., Bo, H. and Sun, H.L. (2014) Improved K-Means Algorithm Based Analysis on Massive Data of Intelligent Power Utilization. Power System Technology, 38, 2715-2720.
- Peng, X.G., Lai, J.W. andChen, Y. (2014) Application of Clustering Analysis in Typical Power Consumption Profile Analysis. Power System Protection and Control, 42, 68-73.
- Guo, X.L. andYu, Y. (2015) A Residential Smart Power Utilization Strategy Based on Cloud Computing. Automation of Electric Power System, 39, 114-119+133.
- Zhang, S.X., Liu, J.M., Zhao, B.Z. and Cao, J.P. (2013) Cloud Computing-Based Analysis on Residential Electricity Consumption Behavior. Power System Technology, 37, 1542-1546.
- Han, J.W. (2012) Data Mining Concepts and Techniques. China Machine Press, Beijing.
- Zhang,X.L., Hao, S.P., Li, J. and Jiang, C.R. (2015) Grey Correlation Based Analysis on Impacting Factors of Maximum Power Point Tracking Control of Wind Power Generating Unit. Power System Technology, 39, 445-449.