Statistical learning methods for photovoltaic energy output prediction
- Authors: Magaya, Aphiwe
- Date: 2024-04
- Subjects: Photovoltaic power generation , Mathematical statistics , Statistics
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64138 , 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. , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
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- Date Issued: 2024-04
A mixed methods investigation of students’ attitudes towards statistics and quantitative research methods: a focus on postgraduate psychology students at a South African university
- Authors: Ngantweni, Xolelwa
- Date: 2020
- Subjects: Fetal alcohol spectrum disorders -- South Africa , Statistics , Psychology -- Research , College students -- South Africa -- Attitudes , Psychology -- Research -- South Africa
- Language: English
- Type: text , Thesis , Masters , MA
- Identifier: http://hdl.handle.net/10962/140490 , vital:37894
- Description: Many University programs offer a course in either basic or intermediate statistics as part of the degree requirements prior to graduation (McGrath, Ferns, Greiner, Wanamaker and Brown, 2015). These statistics or quantitative research methods courses are integral in helping students gain vital skills in analysing quantitative data. Research (Schau, Stevens, Dauphinee, and Del Vecchio, 1995) does however indicate that most students have a perfunctory disposition towards these courses. My study sought to particularly investigate attitudes towards statistics and quantitative research methods amongst a sample of 61 postgraduate Psychology students at Rhodes University undertaking a ‘Quantitative Research Methods’ course as part of their degree offering. A mixed methods approach was used to investigate students’ attitudes towards statistics and quantitative research methods. The Survey of Attitudes Toward Statistics (SATS-36) (Schau, 2003) captured student’s attitudes towards statistics using a Likert Scale instrument; whereas detailed qualitative interviews accentuated findings from the SATS-36. Key quantitative findings from the SATS-36 including students’ perceptions of statistics being a difficult course as well as students having a low affect towards statistics are detailed. Key qualitative findings related to why students experience statistics anxiety such as students’ (1) fear of failing statistics, (2) The late introduction of statistics in the Psychology curriculum, and (3) The role of educator/s in alleviating or promoting feelings of statistics anxiety are noted. The significance of these findings as well as the contributions of the study to the teaching and learning of statistics and quantitative research methods courses at Rhodes University are explored, in light of other studies on the topic of statistics anxiety and attitudes towards statistics/ quantitative research methods.
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- Date Issued: 2020
Statistical methods for the detection of non-technical losses: a case study for the Nelson Mandela Bay Municipality
- Authors: Pazi, Sisa
- Date: 2017
- Subjects: Nonparametric statistics Mathematical statistics , Statistics
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10948/19706 , vital:28939
- Description: Electricity is one of the most stolen commodities in the world. Electricity theft can be defined as the criminal act of stealing electrical power. Several types of electricity theft exist, including illegal connections and bypassing and tampering with energy meters. The negative financial impacts, due to lost revenue, of electricity theft are far reaching and affect both developing and developed countries. . Here in South Africa, Eskom loses over R2 Billion annually due to electricity theft. Data mining and nonparametric statistical methods have been used to detect fraudulent usage of electricity by assessing abnormalities and abrupt changes in kilowatt hour (kWh) consumption patterns. Identifying effective measures to detect fraudulent electricity usage is an active area of research in the electrical domain. In this study, Support Vector Machines (SVM), Naïve Bayes (NB) and k-Nearest Neighbour (KNN) algorithms were used to design and propose an electricity fraud detection model. Using the Nelson Mandela Bay Municipality as a case study, three classifiers were built with SVM, NB and KNN algorithms. The performance of these classifiers were evaluated and compared.
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- Date Issued: 2017
Models for forecasting residential property prices using paired comparisons
- Authors: Mpapela, Sinazo
- Date: 2014
- Subjects: Paired comparisons (Statistics) , Statistics
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10948/5839 , vital:21006
- Description: Residential real estate forecasting has become a part of the larger process of business planning and strategic management. Several studies of housing price trends recommend confining statistical analysis to repeated sales of residential property. This study presents an alternate methodology which combines information only on repeated residential sales regardless of the changes that has been made in the house in-between the sales. Additive and multiplicative models were used to forecast the residential property prices in Nelson Mandela Metropole. Data was collected from various sources and was reconciled into one data set for analysis through a process of data screening.
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- Date Issued: 2014
A simulation study of the behaviour of the logrank test under different levels of stratification and sample sizes
- Authors: Jubane, Ido
- Date: 2013
- Subjects: Statistics , Survival analysis (Biometry)
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11784 , http://hdl.handle.net/10353/d1018558 , http://hdl.handle.net/10353/d1018559 , Statistics , Survival analysis (Biometry)
- Description: In clinical trials, patients are enrolled into two treatment arms. A researcher may be interested in studying the effectiveness of a new drug or the comparison of two drugs for the treatment of a disease. This survival data is later analysed using the logrank test or the Cox regression model to detect differences in survivor functions. However, the power function of the logrank test depends solely on the number of patients enrolled into the study. Because statisticians will always minimise type I and type II errors, a researcher carrying out a clinical trial must define beforehand, the number of patients to be enrolled into the clinical study. Without proper sample size and power estimation a clinical trial may fail to detect a false hypothesis of the equality of survivor functions. This study presents through simulation, a way of power and sample size estimation for clinical trials that use the logrank test for their data analysis and suggests an easy method to estimate power and sample size in such clinical studies. Findings on power analysis and sample size estimation on logrank test are applied to two real examples: one is the Veterans' Administration Lung Cancer study; and the other is the data from a placebo controlled trial of gamma interferon in chronic granulotomous disease.
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- Date Issued: 2013