Short-Term Forecasting of Unplanned Power Outages Using Machine Learning Algorithms: A Robust Feature Engineering Strategy Against Multicollinearity and Nonlinearity

Khathutshelo Steven Sivhugwana () and Edmore Ranganai
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Khathutshelo Steven Sivhugwana: Department of Statistics, University of South Africa, Florida Campus, Johannesburg 1709, South Africa
Edmore Ranganai: Department of Statistics, University of South Africa, Florida Campus, Johannesburg 1709, South Africa

Energies, 2025, vol. 18, issue 18, 1-36

Abstract: Efficient power grid operations and effective business strategies require accurate prediction of power outages. However, predicting outages is a difficult task due to the large amount of heterogeneous, random, intermittent, and non-linear power grid data characterised by highly complex variable relationships. Attempting to simultaneously quantify these characteristics using a conventional single (linear or nonlinear) model may lead to inaccurate and costly results. To address this, we propose a hybrid RVM-WT-AdaBoostRT-RF framework using power grid data from the Electricity Supply Commission (Eskom) of South Africa. To achieve model interpretability, the least absolute shrinkage and selection operator (LASSO) is first applied to remedy the adverse effects of multicollinearity through regularisation and variable selection. Secondly, a random forest (RF) is used to select the top 10 most influential variables for each season for further analysis. A relevance vector machine (RVM) captures complex nonlinear relationships separately for each season, while the wavelet transform (WT) decomposes residuals generated from RVM into different frequency subseries (with reduced noise). These subseries are predicted with minimal bias using AdaBoost with regression and threshold (AdaBoostRT). Finally, we stack RVM, AdaBoostRT, RF, and residual individual predictions using RF as a meta-model to produce the final forecast with minimal error accumulation and efficiency. The comparative study, based on point forecast metrics, the Diebold-Mariano test, and prediction interval widths, shows that the proposed model outperforms vector autoregressive (VAR), RF, AdaBoostRT, RVM, and Naïve models. The study results can be utilised for optimising resource allocation, effective power grid management, and customer alerts.

Keywords: machine learning; forecasting; power outage; load-shedding; South Africa; Eskom (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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