A Functional Data Analysis approach to understand patterns imbedded within various data types
- Authors: Mangisa, Siphumlile
- Date: 2021-04
- Subjects: Gqeberha (South Africa) , Eastern Cape (South Africa) , Data mining
- Language: English
- Type: Doctoral theses , text
- Identifier: http://hdl.handle.net/10948/52091 , vital:43426
- Description: The thesis investigates the use of the novel Functional Data Analysis (FDA) methods in tackling various data types. Strong motivation is provided for the use of interesting opportunities offered by FDA to analyse not only economic data, but generally, data from any domain. The use of these methods is illustrated using three unique self-contained case-studies from econometrics. , Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2021
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- Date Issued: 2021-04
Statistical analysis of electricity demand profiles
- Authors: Mangisa, Siphumlile
- Date: 2013
- Subjects: Electric power consumption -- Forecasting , Energy consumption , Electric power consumption
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10572 , http://hdl.handle.net/10948/d1011548 , Electric power consumption -- Forecasting , Energy consumption , Electric power consumption
- Description: An electricity demand profile is a graph showing the amount of electricity used by customers over a unit of time. It shows the variation in electricity demand versus time. In the demand profiles, the shape of the graph is of utmost importance. The variations in demand profiles are caused by many factors, such as economic and en- vironmental factors. These variations may also be due to changes in the electricity use behaviours of electricity users. This study seeks to model daily profiles of energy demand in South Africa with a model which is a composition of two de Moivre type models. The model has seven parameters, each with a natural interpretation (one parameter representing minimum demand in a day, two parameters representing the time of morning and afternoon peaks, two parameters representing the shape of each peak, and two parameters representing the total energy per peak). With the help of this model, we trace change in the demand profile over a number of years. The proposed model will be helpful for short to long term electricity demand forecasting.
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- Date Issued: 2013