Research And Application Of Urban Power Load Forecasting Based On Spark

With the rapid development of smart grids,the degree of information and intelligence of the power grid is getting higher and higher.The power grid relies on mature measurement technology and sensor technology to deploy a large number of intellectual meters,making data acquisition more and more convenient.At the same time,the data generated by the power grid is not only huge in data volume but also complex in structure,making it more and more difficult for power companies to collect,store,process and analyze these data.In addition,with the rapid development of our country’s economy,the accuracy of urban power load forecasting is becoming more and more important.Although the power grid can provide richer training data to improve the accuracy of the forecasting model,as the amount of data and data dimensions increase,the time consumed for model training and the complexity of the model will also increase dramatically.Therefore,studying the application of big data technology and artificial intelligence technology in power grid load forecasting,so that the power grid can better serve the public,has important practical significance and application value.This article studies the urban power load forecasting method,and designs a load forecasting system to realize the efficient collection,storage and analysis of massive data in the power grid.The research contents are summarized as follows:First,based on the historical power load data of a specific city,analyze the characteristics and influencing factors of the power load by drawing the characteristic diagram of the load,and write a web crawler program to obtain load-related data and use data analysis tools based on the analysis results to analyze and process the collected data.The main purpose of data collection and processing is to provide data with high fidelity and high availability for subsequent predictive model training.Secondly,research the principle of random forest algorithm and the working principle of Spark,and design a parallel random forest algorithm based on the Spark platform to improve the efficiency of model training.At the same time,the effectiveness of the designed algorithm is evaluated.The evaluation process is carried out in a variety of scenarios.The experimental results prove the effectiveness of the parallel algorithm.Finally,research on big data processing tools such as Kafka,Flume,HDFS,Spark and Zookeeper,and integrated the functions of these tools to build a power load forecasting system.The whole system consists of four modules: data transmission,data storage,data calculation and data visualization.At the same time,various functions of the system were tested,and the test results proved the feasibility of the design.