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Enhancing Efficiency of Ensemble Machine Learning Models for Short-Term Load Forecasting through Feature Selection by Nikita N. Sergeev; Pavel V. Matrenin is a scholarly article available to read on EtoBox.

What is Enhancing Efficiency of Ensemble Machine Learning Models for Short-Term Load Forecasting through Feature Selection about?

Electric load forecasting directly affects management planning and is necessary for the reliable functioning of electric power systems. Furthermore, accurate forecasts are required by power companies for electricity generation and distribution planning. This paper compares different approaches to the feature selection process for solving the problem of short-term forecasting of industrial enterprise power consumption using machine learning methods. A comparative analysis of the following machine learning methods was carried out: adaptive boosting over decision trees (AdaBoost), gradient boosting over decision trees (Gradient Boosting), and random forest (Random Forest). The choice of such models is due to the presence of binary features in the original data, which are best handled by decision trees. The selection of the most significant features for these models is especially important as they are prone to overfitting when the input data set is large. The most significant features of the original time series were determined by calculating the correlation coefficients between the predicted hour and the preceding hours. Moreover, the repair schedule is used as an additional feature.

Author

Nikita N. Sergeev; Pavel V. Matrenin

Publisher

IEEE

Published

2022

Language

EN