Predictability of Geomagnetically Induced Currents using neural networks
- Authors: Lotz, Stefan
- Date: 2009
- Subjects: Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5483 , http://hdl.handle.net/10962/d1005269 , Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Description: It is a well documented fact that Geomagnetically Induced Currents (GIC’s) poses a significant threat to ground-based electric conductor networks like oil pipelines, railways and powerline networks. A study is undertaken to determine the feasibility of using artificial neural network models to predict GIC occurrence in the Southern African power grid. The magnitude of an induced current at a specific location on the Earth’s surface is directly related to the temporal derivative of the geomagnetic field (specifically its horizontal components) at that point. Hence, the focus of the problem is on the prediction of the temporal variations in the horizontal geomagnetic field (@Bx/@t and @By/@t). Artificial neural networks are used to predict @Bx/@t and @By/@t measured at Hermanus, South Africa (34.27◦ S, 19.12◦ E) with a 30 minute prediction lead time. As input parameters to the neural networks, insitu solar wind measurements made by the Advanced Composition Explorer (ACE) satellite are used. The results presented here compare well with similar models developed at high-latitude locations (e.g. Sweden, Finland, Canada) where extensive GIC research has been undertaken. It is concluded that it would indeed be feasible to use a neural network model to predict GIC occurrence in the Southern African power grid, provided that GIC measurements, powerline configuration and network parameters are made available.
- Full Text:
- Date Issued: 2009
- Authors: Lotz, Stefan
- Date: 2009
- Subjects: Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5483 , http://hdl.handle.net/10962/d1005269 , Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Description: It is a well documented fact that Geomagnetically Induced Currents (GIC’s) poses a significant threat to ground-based electric conductor networks like oil pipelines, railways and powerline networks. A study is undertaken to determine the feasibility of using artificial neural network models to predict GIC occurrence in the Southern African power grid. The magnitude of an induced current at a specific location on the Earth’s surface is directly related to the temporal derivative of the geomagnetic field (specifically its horizontal components) at that point. Hence, the focus of the problem is on the prediction of the temporal variations in the horizontal geomagnetic field (@Bx/@t and @By/@t). Artificial neural networks are used to predict @Bx/@t and @By/@t measured at Hermanus, South Africa (34.27◦ S, 19.12◦ E) with a 30 minute prediction lead time. As input parameters to the neural networks, insitu solar wind measurements made by the Advanced Composition Explorer (ACE) satellite are used. The results presented here compare well with similar models developed at high-latitude locations (e.g. Sweden, Finland, Canada) where extensive GIC research has been undertaken. It is concluded that it would indeed be feasible to use a neural network model to predict GIC occurrence in the Southern African power grid, provided that GIC measurements, powerline configuration and network parameters are made available.
- Full Text:
- Date Issued: 2009
The covariation of South African and foreign equity returns during bull and bear runs : implications for portfolio diversification
- Authors: Mhlanga, Godfrey
- Date: 2009
- Subjects: Stock exchanges -- South Africa , Portfolio management -- South Africa , Investments -- South Africa
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:944 , http://hdl.handle.net/10962/d1002678
- Description: This study examines the pattern of covariation of the industrial index returns of South Africa and foreign industrial sectors. This follows recent increase in national equity correlations and increases in the influence of industry effects in portfolio diversification. The covariation pattern in returns across industries and countries during both bull and bear runs is examined using correlation analysis to determine if there is a difference between the two epochs. The study presents preliminary evidence of the covariation between sectors during a bear and a bull run. Return covariation among sectors is impelled to a greater extent by country-specific factors than by industry-specific factors, implying the segmentation of industrial sectors. Thus, South African investors can in general gain more if a portfolio comprising shares across industries and countries is held, even if these investors buy shares from similar industries.
- Full Text:
- Date Issued: 2009
- Authors: Mhlanga, Godfrey
- Date: 2009
- Subjects: Stock exchanges -- South Africa , Portfolio management -- South Africa , Investments -- South Africa
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:944 , http://hdl.handle.net/10962/d1002678
- Description: This study examines the pattern of covariation of the industrial index returns of South Africa and foreign industrial sectors. This follows recent increase in national equity correlations and increases in the influence of industry effects in portfolio diversification. The covariation pattern in returns across industries and countries during both bull and bear runs is examined using correlation analysis to determine if there is a difference between the two epochs. The study presents preliminary evidence of the covariation between sectors during a bear and a bull run. Return covariation among sectors is impelled to a greater extent by country-specific factors than by industry-specific factors, implying the segmentation of industrial sectors. Thus, South African investors can in general gain more if a portfolio comprising shares across industries and countries is held, even if these investors buy shares from similar industries.
- Full Text:
- Date Issued: 2009
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