The Application Of Association Rules On Load Variation Mining

With the power system continued to deepen market-oriented, Load forecasting is playing an increasingly important role. The existing load forecasting method can meet the normal daily needs of the load forecast,However, due to the non-load factors,Still need to improve the accuracy of load forecasting. How to fully consider these non-load factors on the impact of load forecasting has become necessary to improve the accuracy of load forecasting problems to be solved. Data mining technology is excavated from a large number of data in the implicit, previously unknown, potentially valuable for decision-making knowledge and rules, Is an effective tool to solve these problems.In this paper, the theoretical basis of data mining, combined with the power load data of the specific characteristics of the industry will be association rules method is applied to the excavation load variation. First, the electricity load data and various factors affecting the integration of data, Constructed in order to analyze the theme of power load data warehouse; Secondly, in order to achieve more efficient data mining analysis, the use of clustering technology, the raw data in the continuous data in discrete processing; Finally, the use of FP-Growth algorithm for frequent sets of historical data search, and strong association rules generation, analysis of all relevant factors on the impact of load.