Stacking strategy-assisted random forest algorithm and its application

Short-term power load forecasting provides important guidance for the improvement of power marketing and control levels of power enterprises. In this paper, a novel method, named RF-TStacking, is proposed to forecast the short-term load. This study starts from the influence factors of the power load, the random forest is applied to estimate the importance of the influence factors of short-term load. Based on Stacking strategy, the integration of LightGBM and random forest is realized to achieve short-term power load forecasting. To improve the generalization ability of the load model, random put back sampling is used to sample each primary learner, and the average value is taken as the result of each primary learner. The Bayesian optimization is used to adjust the super parameters of the model to improve the accuracy of the selection of influencing factors. The load data of a region in northwest China are used for the testing, and it is found that the model can provide stable prediction results.

Automated summary

In this paper, a novel method called RF-TStacking is proposed for short-term load forecasting. The importance of influencing factors of the load is estimated using random forest, and short-term load forecasting is achieved through the integration of LightGBM and random forest. Bayesian optimization is used to improve the accuracy of the selection of influencing factors. Testing on load data from a region in northwest China shows that the model provides stable prediction results.