Optimizing home electricity consumption with user habits analysis

With the development of the national economy and the advancement of urbanization, the demand for household electricity consumption is rising sharply and the structure of the complexity of the trend. In order to help users realize intelligent management of household electricity consumption and improve the efficiency of electricity consumption. This paper proposes a smart home electricity consumption optimization method based on user habit analysis. Firstly, the home energy management system framework and related technologies are introduced, and home load dispatch types are categorized and modeled. Then, a circular coordinate fitting method is used to analyze the data and thus derive the users’ electricity consumption habits, as well as an improved K-mean clustering algorithm to mine the users’ personalized demands. Then, a multi-objective intelligent power consumption optimization model is established, and an improved artificial bee colony algorithm is used to solve practical problems. Finally, simulation experiments using real household electricity consumption datasets are conducted to verify the validity and feasibility of the method. The method in this paper can formulate power consumption plans according to the needs of different families, improve the convenience and comfort of users’ power consumption, and realize more reasonable and energy-saving power consumption through users’ participation in adjusting and improving their habitual behaviors. This research has application value and promotion significance and can be widely used in the field of smart home and energy saving and emission reduction.