Forecasting hourly electricity demand in South Africa using machine learning models
dc.contributor.advisor | Sigauke, Caston | |
dc.contributor.advisor | Bere, Alphonce | |
dc.contributor.author | Thanyani, Maduvhahafani | |
dc.date.accessioned | 2020-10-05T12:46:16Z | |
dc.date.available | 2020-10-05T12:46:16Z | |
dc.date.issued | 2020-08-12 | |
dc.description | MSc (Statistics) | en_ZA |
dc.description | Department of Statistics | |
dc.description.abstract | Short-term load forecasting in South Africa using machine learning and statistical models is discussed in this study. The research is focused on carrying out a comparative analysis in forecasting hourly electricity demand. This study was carried out using South Africa’s aggregated hourly load data from Eskom. The comparison is carried out in this study using support vector regression (SVR), stochastic gradient boosting (SGB), artificial neural networks (NN) with generalized additive model (GAM) as a benchmark model in forecasting hourly electricity demand. In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso). The SGB model yielded the least root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) on testing data. SGB model also yielded the least RMSE, MAE and MAPE on training data. Forecast combination of the models’ forecasts is done using convex combination and quantile regres- sion averaging (QRA). The QRA was found to be the best forecast combination model ibased on the RMSE, MAE and MAPE. | en_ZA |
dc.description.sponsorship | NRF | en_ZA |
dc.format.extent | 1 online resource (xiii, 74 leaves : color illustrations) | |
dc.identifier.apacitation | Thanyani, M. (2020). <i>Forecasting hourly electricity demand in South Africa using machine learning models</i>. (). . Retrieved from http://hdl.handle.net/11602/1595 | en_ZA |
dc.identifier.chicagocitation | Thanyani, Maduvhahafani. <i>"Forecasting hourly electricity demand in South Africa using machine learning models."</i> ., , 2020. http://hdl.handle.net/11602/1595 | en_ZA |
dc.identifier.citation | Thanyani, Maduvhahafani (2020) Forecasting hourly electricity demand in South Africa using machine learning models. University of Venda, South Africa.<http://hdl.handle.net/11602/1595>. | |
dc.identifier.ris | TY - Dissertation AU - Thanyani, Maduvhahafani AB - Short-term load forecasting in South Africa using machine learning and statistical models is discussed in this study. The research is focused on carrying out a comparative analysis in forecasting hourly electricity demand. This study was carried out using South Africa’s aggregated hourly load data from Eskom. The comparison is carried out in this study using support vector regression (SVR), stochastic gradient boosting (SGB), artificial neural networks (NN) with generalized additive model (GAM) as a benchmark model in forecasting hourly electricity demand. In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso). The SGB model yielded the least root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) on testing data. SGB model also yielded the least RMSE, MAE and MAPE on training data. Forecast combination of the models’ forecasts is done using convex combination and quantile regres- sion averaging (QRA). The QRA was found to be the best forecast combination model ibased on the RMSE, MAE and MAPE. DA - 2020-08-12 DB - ResearchSpace DP - Univen KW - Electricity demand forecasting KW - Machine learning KW - Lasso KW - Neutral networks KW - Support vector regression KW - Forecasting KW - Forecast combination LK - https://univendspace.univen.ac.za PY - 2020 T1 - Forecasting hourly electricity demand in South Africa using machine learning models TI - Forecasting hourly electricity demand in South Africa using machine learning models UR - http://hdl.handle.net/11602/1595 ER - | en_ZA |
dc.identifier.uri | http://hdl.handle.net/11602/1595 | |
dc.identifier.vancouvercitation | Thanyani M. Forecasting hourly electricity demand in South Africa using machine learning models. []. , 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/1595 | en_ZA |
dc.language.iso | en | en_ZA |
dc.rights | University of Venda | |
dc.subject | Electricity demand forecasting | en_ZA |
dc.subject | UCTD | en_ZA |
dc.subject | Lasso | en_ZA |
dc.subject | Neutral networks | en_ZA |
dc.subject | Support vector regression | en_ZA |
dc.subject | Forecasting | en_ZA |
dc.subject | Forecast combination | en_ZA |
dc.subject.ddc | 333.7930968 | |
dc.subject.lcsh | Electricity -- South Africa | |
dc.subject.lcsh | Electrification -- South Africa | |
dc.subject.lcsh | Electric power distribution -- South Africa | |
dc.subject.lcsh | Electric power consumption -- South Africa | |
dc.subject.lcsh | Electric power production -- South Africa | |
dc.subject.lcsh | Renewable energy resources -- South Africa | |
dc.title | Forecasting hourly electricity demand in South Africa using machine learning models | en_ZA |
dc.type | Dissertation | en_ZA |