Forecasting hourly solar irradiance in South Africa using machine learning models
| dc.contributor.advisor | Sigauke, Caston | |
| dc.contributor.advisor | Mbuvha, Rendani | |
| dc.contributor.author | Mutavhatsindi, Tendani | |
| dc.date | 2020 | |
| dc.date.accessioned | 2021-02-02T13:46:17Z | |
| dc.date.available | 2021-02-02T13:46:17Z | |
| dc.date.issued | 2020-08-12 | |
| dc.description | MSc (Statistics) | en_ZA |
| dc.description | Department of Statistics | |
| dc.description.abstract | Solar irradiance forecasting is essential in renewable energy grids amongst others for back-up programming, operational planning, and short-term power purchases. This research study focuses on forecasting hourly solar irradiance using data obtained from the Southern African Universities Radiometric Network at the University of Pretoria radiometric station. This research project compares the predictive performance of long short-term memory (LSTM) networks, support vector regression (SVR), and feed forward neural networks (FFNN) model for forecasting short-term solar irradiance. While all the models outperform principal component regression model, a benchmark model in this study, the FFNN yields the lowest mean absolute error (MAE) and root mean square error (RMSE) on the testing set. According to findings, among the three fitted machine learning models, the FFNN model produced the best forecast accuracy based on MAE and RMSE. Forecast combination of machine learning models’ forecasts is done using convex combination and quantile regression averaging (QRA). Based on MAE and average pinball losses, the QRA forecast combination model is the best forecast combination method, and also the best forecasting model compared with the individual machine learning models. Further analysis of the prediction interval widths (PIWs) based on the prediction interval coverage probabilities (PICPs), and prediction interval normalised average widths (PINAWs) including a count of the number of predictions below and above the PIs show inconsistency. The best model based on PINAW at 90% is QRA, FFNN at 95%, and SVR at 99%. There is inconsistency in the results of PICPs and PINAWs for different PINC values. The residual analysis shows FFNN as the best model with narrowest error distribution compared to other models, followed by QRA. | en_ZA |
| dc.description.sponsorship | NRF | en_ZA |
| dc.format.extent | 1 online resource (xiii, 75 leaves : color illustrations, color maps) | |
| dc.identifier.apacitation | Mutavhatsindi, T. (2020). <i>Forecasting hourly solar irradiance in South Africa using machine learning models</i>. (). . Retrieved from http://hdl.handle.net/11602/1664 | en_ZA |
| dc.identifier.chicagocitation | Mutavhatsindi, Tendani. <i>"Forecasting hourly solar irradiance in South Africa using machine learning models."</i> ., , 2020. http://hdl.handle.net/11602/1664 | en_ZA |
| dc.identifier.citation | Mutavhatsindi Tendani (2020) Forecasting hourly solar irradiance in South Africa using machine learning models. University of Venda, South Africa,<http://hdl.handle.net/11602/1664>. | |
| dc.identifier.ris | TY - Dissertation AU - Mutavhatsindi, Tendani AB - Solar irradiance forecasting is essential in renewable energy grids amongst others for back-up programming, operational planning, and short-term power purchases. This research study focuses on forecasting hourly solar irradiance using data obtained from the Southern African Universities Radiometric Network at the University of Pretoria radiometric station. This research project compares the predictive performance of long short-term memory (LSTM) networks, support vector regression (SVR), and feed forward neural networks (FFNN) model for forecasting short-term solar irradiance. While all the models outperform principal component regression model, a benchmark model in this study, the FFNN yields the lowest mean absolute error (MAE) and root mean square error (RMSE) on the testing set. According to findings, among the three fitted machine learning models, the FFNN model produced the best forecast accuracy based on MAE and RMSE. Forecast combination of machine learning models’ forecasts is done using convex combination and quantile regression averaging (QRA). Based on MAE and average pinball losses, the QRA forecast combination model is the best forecast combination method, and also the best forecasting model compared with the individual machine learning models. Further analysis of the prediction interval widths (PIWs) based on the prediction interval coverage probabilities (PICPs), and prediction interval normalised average widths (PINAWs) including a count of the number of predictions below and above the PIs show inconsistency. The best model based on PINAW at 90% is QRA, FFNN at 95%, and SVR at 99%. There is inconsistency in the results of PICPs and PINAWs for different PINC values. The residual analysis shows FFNN as the best model with narrowest error distribution compared to other models, followed by QRA. DA - 2020-08-12 DB - ResearchSpace DP - Univen KW - Forecast combination KW - Machine learning KW - Neural networks KW - Solar irradiance KW - Support vector regression LK - https://univendspace.univen.ac.za PY - 2020 T1 - Forecasting hourly solar irradiance in South Africa using machine learning models TI - Forecasting hourly solar irradiance in South Africa using machine learning models UR - http://hdl.handle.net/11602/1664 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11602/1664 | |
| dc.identifier.vancouvercitation | Mutavhatsindi T. Forecasting hourly solar irradiance in South Africa using machine learning models. []. , 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/1664 | en_ZA |
| dc.language.iso | en | en_ZA |
| dc.rights | University of Venda | |
| dc.subject | Forecast combination | en_ZA |
| dc.subject | UCTD | en_ZA |
| dc.subject | Neural networks | en_ZA |
| dc.subject | Solar irradiance | en_ZA |
| dc.subject | Support vector regression | en_ZA |
| dc.title | Forecasting hourly solar irradiance in South Africa using machine learning models | en_ZA |
| dc.type | Dissertation | en_ZA |