Study On Optimal Scheduling Of Smart Home Energy Management System In Towns Around Qinghai Lake Area

The design of urban habitat units in the Qinghai Lake area is an important part of the planning of the plateau beautiful town system.From the perspective of planning and design,Habitat units are built with the goal of facilitating the green and healthy development of the ecosystem,and the town smart home oriented therein is the future development direction.In this paper,we study the optimal scheduling strategy of urban smart home energy management system in the Qinghai Lake area for the low efficiency of electricity consumption and energy waste caused by improper energy management in urban residential houses,and the main contents are as follows.(1)Research on smart home energy management system.To address the problem of improper home energy management in towns around Qinghai Lake region,based on the energy consumption characteristics and environmental features of urban residents in the region,we study the smart home energy management system in towns in the region,design the functions of the home energy management system in towns around Qinghai Lake region,determine the system optimization scheduling content and complete the system interaction interface to facilitate users to better participate in home electricity management.(2)A PV power prediction model is established.To address the problem of low accuracy of PV prediction,firstly,Spearman correlation analysis is used to screen out the key influencing factors of PV power generation;then,Affinity Propagation(AP)algorithm is applied to select data samples with similar characteristics to the prediction date;finally,Genetic Algorithm(GA,Finally,Genetic Algorithm(GA)is used to optimize the Radial Basis Function(RBF)neural network parameters to establish the AP-GA-RBF PV power generation prediction model.The simulation results show that compared with the RBF and GA-RBF models,the root mean square relative error index of the model is reduced by 53.6% and 21.43%,and the average absolute percentage error index is reduced by 51.13% and 21.13%,respectively,which significantly improves the prediction accuracy and lays the foundation for the reliability of the energy management system scheduling results.(3)The multi-objective optimal scheduling strategy for smart home energy management system is proposed.For the multi-objective optimization problem of home appliances,firstly,the home appliances commonly used by urban residents in the Qinghai Lake Rim area are classified and an equipment dispatching model is established;secondly,the system optimization model is established with the objectives of minimizing the user’s electricity cost and the load-to-peak ratio of household-grid interaction and maximizing the satisfaction of electricity consumption;finally,the model is solved by an improved beetle antennae search algorithm to obtain the system optimization dispatching strategy.The simulation results show that,compared with the pre-dispatch period,the customer’s revenue for one day after dispatch reaches ￥7.43,the satisfaction of electricity consumption increases by 11.8%,and the peak-to-average ratio of household-grid interaction decreases by 42.69%,realizing the efficient and economic operation of home appliances.In summary,the accurate prediction of PV power generation and the multi-objective optimal scheduling of energy management system have important reference value for the energy-saving management of smart homes,and are of great significance for the improvement of the habitat environment in the Qinghai Lake area.