Design And Implementation Of An Electrical Energy Monitoring System Based On The Cloud Platform

As global energy resources are decreasing,the demand for electricity from urban residents is gradually increasing,and it is a hot topic of research to achieve energy saving by allocating electricity resources according to demand.This paper builds a cloud-based electricity monitoring system to remotely obtain load data of regional users,analyse and process the data,and design corresponding algorithms to make short-term regional load forecasts,reasonably allocate electricity resources according to the forecast results,and avoid energy wastage.The main research elements of the thesis are as follows:Firstly,the application requirements of the cloud-based power monitoring system are analysed and the overall system architecture is designed for the different functional requirements.Then,the overall scheme design of the system was carried out,and the working process and communication protocols of the system were analysed.Finally,the advantages and disadvantages of several common data communication technologies are compared and analysed,from which the Lo Ra communication technology with advantages such as long transmission distance and low power consumption is selected.Secondly,a study of regional load forecasting is carried out.The characteristics and influencing factors of electricity consumption data are analysed,and a combined load forecasting model based on K-Means++ clustering and genetic algorithm optimisation is proposed for BP neural networks.The model uses the K-Means++ clustering method to cluster historical load data,and the clustered data set and the conditional influencing factors are jointly used as input to the electricity consumption forecasting model,so as to lighten the data set.In addition,this paper uses a genetic algorithm to optimise the weights and thresholds of the BP neural network to improve the problem that the BP neural network tends to converge to a local optimum when training the data set.The effectiveness of the model proposed in this paper is verified through simulation experiments.Finally,in order to verify the practical application of the system designed in this paper,an electrical energy monitoring system based on a cloud platform was built.A So C-based power harvesting device is designed,which overcomes the disadvantages of high cost and complex design of power harvesting devices designed using discrete solutions.A block diagram of the hardware design of the power harvesting device is given,and the design of the main functional circuits is analysed in detail,mainly including the design of the minimum system and the design of the functional circuits.The general idea of the software design is introduced,and the design of the software of the system power collection terminal,the APP of the user terminal and the Web of the management terminal are introduced in detail,mainly realising the sub-functions of real-time monitoring of power consumption information,remote management of power consumption equipment and issuing power consumption plans.In summary,this paper designs and implements an electrical energy monitoring system based on the cloud platform,which can collect and transmit the user’s electricity consumption data to the cloud server in real time,and display,process and forecast electricity consumption on the monitoring platform.