Research And Realization Of Power Financial Forecast System Based On Business Intelligence

With the explosive growth of business information, the power corporate's business environment has undergone tremendous changes. How to dig out effective decision-making from large amounts of information , it become an urgent problem. The developed power company's financial management system is also facing the plight of the lack of decision support system. Business intelligence is a high-end technology in the field of information, and become the most effective "Nuggets device".This paper is on the background of electric power enterprise's financial resource management, betakes the technology of business intelligence, which is combined with data mining technology. The solution, design and realization of financial forecast business intelligence system is provided in this paper. This article builds the financial management data warehouse based on SAP BW platform by the thorough analysis of the electric power enterprise's financial management from the assets, amounts due, purchase and sale of electric aspects, and designs the total framework. Using OLAP technology ,this paper enables the analysis of financial data based on the data warehouse. At the same time, the paper study in-depth the regression analysis based on genetic algorithms. For its lack,the paper combine the genetic algorithm and particle swarm algorithm and establish a regression analysis forecast model based on genetic particle swarm optimization algorithm. To achieve the forecast indicators of financial data, the forecast model is imported into SAP data mining workbench. The results show that the forecasting result of the prediction model presented in this paper is more accurate than the regression analysis-based genetic algorithm.It is a feasible and effective regression prediction model.This article provides a all-sided and intuitionistic accordings to financial resource forecast,and resolve the information acnode in the ERP system. We can provide a sound business decision applications services, and can promote financial management processes optimization and improvement.