- Title
- Statistical learning methods for photovoltaic energy output prediction
- Creator
- Magaya, Aphiwe
- Subject
- Photovoltaic power generation
- Subject
- Mathematical statistics
- Subject
- Statistics
- Date Issued
- 2024-04
- Date
- 2024-04
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/64138
- Identifier
- vital:73656
- Description
- Predicting solar energy accurately is important for the integration of more renewable energy into the grid, which can help to alleviate the energy demand on traditional coal-powered sources in South Africa. This study aims to assess several statistical learning models to predict the energy output of a 1MW photovoltaic system installed on the Nelson Mandela University South Campus in Gqeberha. Weather data (including temperature, wind speed, wind direction, precipitation, air pressure, and humidity) and solar irradiance data (including global horizontal radiation, diffuse radiation, and direct radiation) are used to predict the energy output of this system using Artificial Neural Networks (ANN), Support Vector Machines (SVM), Multiple Linear Regression (MLR), and Regression Trees (RT). The performance of each of the models was compared and the results indicated that the ANN model performed best.
- Description
- Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (xiv, 121 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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NMU School of Computer Science, Mathematics, Physics and Statistics
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Magaya, A.pdf | 2 MB | Adobe Acrobat PDF | View Details Download |