Abstract:

A healthy data set is acquired through data collection and preprocessing based on the construction of distributed big data analysis platform such as Hadoop, Spark and Hbase. Regression forecasting model of energy consumption based on the parallel random forest algorithm is built to comprehensively analyze and compare the relationship between input based on random forest prediction model, model parameters and output. The emphasis lies on comparative analysis of the decision tree number, depth of the decision tree and maximum number of split, which will affect the training model accuracy, running time and complexity. Optimization of the prediction model can achieve accurate prediction on the coal consumption for power supply and soft measurement calculation.

Key words:

big data analysis platform, random forest regression algorithm, Spark distributed computing, coal consumption, prediction