Using machine learning methods to forecast the number of power outages at substations

Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 18, Kremlyovskaya street, Kazan, 420008, Russia

^* Corresponding author: mchulpan@gmail.com

Abstract

Forecasting in the energy sector is of great importance for suppliers and for consumers. Optimum power consumption depends on many factors. Due to natural or any other external conditions, accidents are possible. In order to minimize emergency consequences, it is necessary to be prepared for possible outages in advance in order to reduce the time for their elimination and decision-making. This article considers the problem of forecasting power outages at substations. The enterprise provided a summary table of outages at substations due to natural disasters on specific days. To solve the problem, a machine learning method was chosen – binary classification. Five different algorithms were considered. The models were tested on data from the first half of 2022. The most effective algorithm for 20% of the test sample was the binary classification algorithm using generalized additive models (GAM). This algorithm is also one of the best with a sample of 50%. A model has been prepared for further use in predicting the probability of outages at the enterprise. The model can be used in other organizations; for this, it is first necessary to train the model on the data of the corresponding region.