Solar power forecasting using Gaussian process regression
| 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.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.identifier.apacitation | Chandiwana, E. (2023). <i>Solar power forecasting using Gaussian process regression</i>. (). . Retrieved from http://hdl.handle.net/11602/2581 | en_ZA |
| dc.identifier.chicagocitation | Chandiwana, Edina. <i>"Solar power forecasting using Gaussian process regression."</i> ., , 2023. http://hdl.handle.net/11602/2581 | en_ZA |
| 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.ris | TY - Thesis AU - Chandiwana, Edina AB - 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. DA - 2023-10-05 DB - ResearchSpace DP - Univen KW - Core vector regression KW - Gaussian process KW - Minimum enclosed ball KW - Quantile regression KW - Solar power KW - Spatio-temporal LK - https://univendspace.univen.ac.za PY - 2023 T1 - Solar power forecasting using Gaussian process regression TI - Solar power forecasting using Gaussian process regression UR - http://hdl.handle.net/11602/2581 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11602/2581 | |
| dc.identifier.vancouvercitation | Chandiwana E. Solar power forecasting using Gaussian process regression. []. , 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2581 | en_ZA |
| dc.language.iso | en | en_ZA |
| dc.relation.requires | ||
| dc.rights | University of Venda | |
| dc.subject | Core vector regression | en_ZA |
| dc.subject | UCTD | 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 |