Research On Residential Intelligent Power Consumption System Based On Edge Computing

At present,the field of electric power energy is in an important period of development in the direction of high efficiency,environmental protection,low carbon and diversification.The electric power industry is striding forward with service-oriented,energy-saving,economic and safe electricity as the core,and the focus of household power consumption will also shift to the participation of the whole people.But at present,most families do not have the means and conditions to participate in the intelligent power consumption,and can not effectively control the household power load.The traditional intelligent power system based on cloud computing transmits a large amount of data to the cloud for processing.The response speed of user request is slow,the channel transmission pressure is high,and the privacy of home data cannot be guaranteed.Therefore,this article proposes to integrate edge computing into residential power consumption system,and provides an optimization scheme for edge processing of power consumption data,so as to meet the flexibility,security and efficiency required by equipment connection and data processing,provide energy consumption suggestions for users,realize economic power consumption and facilitate peak load reduction.The main work of this article is as follows:1.A residential intelligent power system architecture design scheme based on edge computing is proposed.Raspberry Pi is selected as the hardware platform of home gateway to undertake edge computing.The intelligent power optimization algorithm is deployed in the edge terminal device raspberry pie to process and analyze the data collected by the intelligent socket,and to manage the nearby energy saving and intelligent decision-making control of the connected devices.It can process power consumption data at the edge of home,relieve the pressure of data processing in cloud center,accelerate the response time of user request and intelligent device,and ensure the security of data transmission.2.Fully considering the electricity demand and habits of household users,taking the electricity cost and comfort as the objective function,a multi-objective mixed integer linear programming based optimization strategy for household electricity consumption is proposed.Firstly,according to the schedulability of different household loads,the electricity tariff model under TOU price is constructed.Secondly,the user’s electric comfort is quantified,and the comfort model is established,which is converted into penalty cost,combining the electric charge model with the electric comfort model.Finally,the genetic algorithm is improved,and the hybrid genetic algorithm based on simulated annealing is used to solve the model,and the simulation is carried out under the time of use price mechanism.3.Design and implement the residential intelligent power consumption system based on edge computing,put forward the specific implementation scheme of functional modules,and complete the development and effect verification of software application APP.Through simple operation,the user can know the operation status of each household appliance,and can remotely control and optimize it in a planned way;the user can flexibly set relevant parameters for each household appliance,so as to achieve energy-saving,carbon reduction,reducing electricity expenditure and humanized power distribution.