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Solar power forecasting using Gaussian process regression

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dc.contributor.advisor Sigauke, Caston
dc.contributor.advisor Bere, Alphonce
dc.contributor.author Chandiwana, Edina
dc.date 2023
dc.date.accessioned 2023-10-22T19:58:00Z
dc.date.available 2023-10-22T19:58:00Z
dc.date.issued 2023-10-05
dc.identifier.citation Chandiwana, E. (2023). Solar power forecasting using Gaussian process regression. University of Venda, Thohoyandou, South Africa.<http://hdl.handle.net/11602/2581>.
dc.identifier.uri http://hdl.handle.net/11602/2581
dc.description PhD (Statistics) en_ZA
dc.description Department of Mathematical and Computational Sciences
dc.description.abstract Solar power forecasting has become an important aspect affecting crucial day-to-day activities in people's lives. Many African countries are now facing blackouts due to a shortage of energy. This has caused the urge to encourage people to use other energy sources to rise, resulting in different energy inputs into the main electricity grid. When the number of power sources being fed into the main grid increases, so does the need for efficient methods of forecasting these inputs. Thus, there is a need to come up with efficient prediction techniques inorder to facilitate proper grid management. The main goal of this thesis is to explore how Gaussian process predicting frameworks can be developed and used to predict global horiz0ontal irra- diance. Data on Global horizontal irrandiance and some weather variables collected from various meterological stations were made available through SAURAN (Southern African Universities Radiometric Network). The length of the dataset ranged from 496 to 17325 datapoints. Ve proposed using Gaussian process regression (GPR) to predict solar power generation. In South Africa, studies based on GPR regarding forecasting solar power are still very few, and more needs to be done in this area. At first, we explored covariance function selection, and a GPR was developed using Core vector regression (CVR). The predictions produced through this method were more accurate than the benchmark models used: Gradient Boosting Regression (GBR) and Support Vector Regression then, we explored interval estimation, Quantile re- gression and GPR were coupled in order to develop the modelling framework. This was also done to improve the accuracy of the GPR models. The results proved that the model performed better than the Bayesian Structural Time Series Regression. Ve also explored spatial dependence; spatio-temporal regression was incorporated into the modelling framework coupled with GPR. This was done to incorporate various weather stations' conditions into the modelling process. The spatial analysis results proved that GPR coupled with spatial analysis produced results that were superior to the Autoregressive Spatial analysis and benchmark model used: Linear Spatial analysis. The GPR results had accuracy measures that proved superior to the benchmark models. Various other tools were used to improve the accuracy of i the GPR results. This includes the use of combining forecasts and standardisation of predictions. The superior results indicate a vast benefit economic-wise because it allows those who manage the power grid to do so effectively and efficiently. Effective power grid management reduces power blackouts, thus benefitting the nation eco- nomically and socially. en_ZA
dc.description.sponsorship National Research Foundation (NRF) en_ZA
dc.format.extent 1 online resource (xvii, 204 leaves) : color illustrations, color maps
dc.language.iso en en_ZA
dc.relation.requires PDF
dc.rights University of Venda
dc.subject Core vector regression en_ZA
dc.subject Gaussian process en_ZA
dc.subject Minimum enclosed ball en_ZA
dc.subject Quantile regression en_ZA
dc.subject Solar power en_ZA
dc.subject Spatio-temporal en_ZA
dc.subject.ddc 621.47
dc.subject.lcsh Solar energy
dc.subject.lcsh Solar power plants
dc.subject.lcsh Solar cells
dc.subject.lcsh Gaussian processes
dc.subject.lcsh Electric power-plants
dc.subject.lcsh Direct energy conversion
dc.subject.lcsh Distribution Probability theory)
dc.subject.lcsh Stochastic processes
dc.subject.lcsh Gaussian measures
dc.title Solar power forecasting using Gaussian process regression en_ZA
dc.type Thesis en_ZA


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