Short-term hourly load forecasting in South Africa using neutral networks
| dc.contributor.author | Ilunga, Elvis Tshiani | |
| dc.date.accessioned | 2018-09-10T11:26:19Z | |
| dc.date.available | 2018-09-10T11:26:19Z | |
| dc.date.issued | 2018 | |
| dc.description | 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. | en_ZA |
| dc.description.abstract | 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. | en_ZA |
| dc.description.librarian | LG2018 | en_ZA |
| dc.format.extent | Online resource (xi, 83 pages) | |
| dc.identifier.citation | Ilunga, Elvis Tshiani (2018) Short-term hourly load forecasting in South Africa using neural networks, University of the Witwatersrand, Johannesburg, https://hdl.handle.net/10539/25629 | |
| dc.identifier.uri | https://hdl.handle.net/10539/25629 | |
| dc.language.iso | en | en_ZA |
| dc.subject.lcsh | Neural networks (Computer science) | |
| dc.subject.lcsh | Pattern perception | |
| dc.title | Short-term hourly load forecasting in South Africa using neutral networks | en_ZA |
| dc.type | Thesis | en_ZA |
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