Citation: Motepe, S.; Hasan, A.N.;
Shongwe, T. Forecasting the Total
South African Unplanned Capability
Loss Factor Using an Ensemble of
Deep Learning Techniques. Energies
2022, 15, 2546. https://doi.org/
10.3390/en15072546
Academic Editors: Luis
Hernández-Callejo, Sergio
Nesmachnow and Sara Gallardo
Saavedra
Received: 31 January 2022
Accepted: 7 March 2022
Published: 31 March 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Forecasting the Total South African Unplanned Capability Loss
Factor Using an Ensemble of Deep Learning Techniques
Sibonelo Motepe
1,
* , Ali N. Hasan
2
and Thokozani Shongwe
1
1
Department of Electrical and Electronic Engineering Technology, Faculty of Engineering and
the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa; tshongwe@uj.ac.za
2
Department of Electrical Engineering, Faculty of Engineering Science and Technology,
Higher Colleges of Technology, Abu Dhabi 25026, United Arab Emirates; alinabeal99@gmail.com
* Correspondence: djscvii@gmail.com
Abstract:
Unplanned power plant failures have been seen to be a major cause of power shortages,
and thus customer power cuts, in the South African power grid. These failures are measured
as the unplanned capability loss factor (UCLF). The study of South Africa’s UCLF is almost non-
existent. Parameters that affect the future UCLF are, thus, still not well understood, making it
challenging to forecast when power shortages may be experienced. This paper presents a novel study
of South African UCLF forecasting using state-of-the-art deep learning techniques. The study further
introduces a novel deep learning ensemble South African UCLF forecasting system. The performance
of three of the best recent forecasting techniques, namely, long short-term memory recurrent neural
network (LSTM-RNN), deep belief network (DBN), and optimally pruned extreme learning machines
(OP-ELM), as well as their aggregated ensembles, are investigated for South African UCLF forecasting.
The impact of three key parameters (installed capacity, demand, and planned capability loss factor)
on the future UCLF is investigated. The results showed that the exclusion of installed capacity in
the LSTM-RNN, DBN, OP-ELM, and ensemble models doubled the UCLF forecasting error. It was
also found that an ensemble model of two LSTM-RNN models achieved the lowest errors with a
symmetric mean absolute percentage error (sMAPE) of 6.43%, mean absolute error (MAE) of 7.36%,
and root-mean-square error (RMSE) of 9.21%. LSTM-RNN also achieved the lowest errors amongst
the individual models.
Keywords: deep learning; forecasting; power outages; coal power plants; recurrent neural networks;
ensemble techniques
1. Introduction
South Africa has been seen to be a late participant in the three key industrial revolu-
tions [
1
]. The use of artificial intelligence (AI) and data is on the rise in South Africa [
2
–
4
].
This rise means that South Africa might not be a late participant in the fourth industrial
revolution. In 2007, 2013, 2018, and 2019, South Africa experienced a shortage in power
supply due to various challenges, leading to load shedding [
1
]. South Africa’s public power
utility, Eskom, has on several occasions stated its inability to accurately predict/forecast the
unplanned capability loss factor (UCLF) as one of the major factors leading to an unreliable
power supply and unpredictable load shedding [
5
,
6
]. UCLF is a term that refers to the
measure of unplanned plant breakdown. The behavior of South African UCLF has not
been well studied. Pretorius et al. studied the impact of the South African energy crisis
on emissions [
7
]. This study only talks about an increase in UCLF due to maintenance
deferral. The study does not talk about how to forecast UCLF, nor the major factors that
contribute to UCLF that can help in the forecasting of UCLF. The UCLF, planned capability
loss factor (PCLF), and other capability loss factor (OCLF), together with the installed ca-
pacity, determine the power available to supply customers. The PCLF is the planned plant
Energies 2022, 15, 2546. https://doi.org/10.3390/en15072546 https://www.mdpi.com/journal/energies
