A research report submitted to the Faculty of Science, University of the Witwatersrand,
Johannesburg, in partial fulfilment of the requirements for the degree of Master of
Science,
Johannesburg, 30 March 2018. / Accuracy of the load forecasts is very critical in the power system industry, which is the
lifeblood of the global economy to such an extent that its art-of-the-state management is the
focus of the Short-Term Load Forecasting (STLF) models.
In the past few years, South Africa faced an unprecedented energy management crisis that
could be addressed in advance, inter alia, by carefully forecasting the expected load demand.
Moreover, inaccurate or erroneous forecasts may result in either costly over-scheduling or
adventurous under-scheduling of energy that may induce heavy economic forfeits to power
companies. Therefore, accurate and reliable models are critically needed.
Traditional statistical methods have been used in STLF but they have limited capacity to
address nonlinearity and non-stationarity of electric loads. Also, such traditional methods
cannot adapt to abrupt weather changes, thus they failed to produce reliable load forecasts in
many situations.
In this research report, we built a STLF model using Artificial Neural Networks (ANNs) to
address the accuracy problem in this field so as to assist energy management decisions makers
to run efficiently and economically their daily operations. ANNs are a mathematical tool that
imitate the biological neural network and produces very accurate outputs.
The built model is based on the Multilayer Perceptron (MLP), which is a class of feedforward
ANNs using the backpropagation (BP) algorithm as its training algorithm, to produce accurate
hourly load forecasts. We compared the MLP built model to a benchmark Seasonal
Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) model
using data from Eskom, a South African public utility. Results showed that the MLP model,
with percentage error of 0.50%, in terms of the MAPE, outperformed the SARIMAX with
1.90% error performance. / LG2018
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/25629 |
| Date | January 2018 |
| Creators | Ilunga, Elvis Tshiani |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | Online resource (xi, 83 pages), application/pdf, application/pdf |
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