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Electric Power Forecasting Based on Logistic Regression and Gradient Boost Regression Tree

With the gradual marketization of power industry, power output forecasting is becoming more and more important in the power industry. The accurate power output of the power plant can be more reasonable for the power plant to make relevant decisions ensure the safe operation of the power plant. At the same time, the cost savings for the power plant to obtain more profits. In order to achieve the prediction of power output of the power plant, this paper uses logistics regression and gradient boosting regression tree on the basis of comprehensive analysis of the characteristics cause the power output to predict the power output of the public data set provided by a power plant. In the end, the average percentage error, root mean square error and relative value evaluation indexes is used to quantitatively analyze the prediction results and to evaluate the achievement of the two models in the prediction of electric energy output.