Caston Sigauke | University of Venda (original) (raw)

Papers by Caston Sigauke

http://scholar.google.com/citations?user=OKgPPJAAAAAJ&hl=en

African Journal of Science, Technology, Innovation and Development, Dec 1, 2020

In the early stage of business, which is where most new ventures fail, many entrepreneurs experie... more In the early stage of business, which is where most new ventures fail, many entrepreneurs experience discrepancies between their entrepreneurial expectations and business realities. These discrepancies referred to by this paper as an entrepreneurial gap (EG) are, therefore, among other factors, professed to be responsible for the high attrition rate of emerging ventures in South Africa. An oversight in this area of EG, despite the provision of most required resources, may still lead to business failure. This paper argues that there is more yet to be comprehended regarding early-stage business success, concerning the entrepreneur component. The purpose of this paper was to recognize and classify factors responsible for establishing entrepreneurial gaps with the intent to improve the level of preparedness among emerging entrepreneurs. A qualitative approach with in-depth interviews was employed in the data collection. ATLAS ti 8 was used to unpack factors that instigate entrepreneurial gaps while posing challenges to emerging entrepreneurs in the early stage of business. The groups identified were: entrepreneur management, familism and personal management. The findings provide information that is credible to improving the level of preparedness among emerging entrepreneurs, and could be used by mentors, coaches and relevant support structures.

American Journal of Infection Control, Mar 1, 2023

BackgroundThis study aims to show that including pairwise hierarchical interactions of covariates... more BackgroundThis study aims to show that including pairwise hierarchical interactions of covariates and combining forecasts from individual models improves prediction accuracy.MethodsThe least absolute shrinkage and selection operator via hierarchical pairwise interaction is used in selecting variables that are not correlated and with the greatest predictive power in single forecast models (Gradient boosting method [GBM], Generalized additive models [GAMs], Support vector regression [SVR]) are used in the analysis. The best model was selected based on the mean absolute error (MAE), the best key performance indicator for skewed data. Forecasts from the 5 models were combined using linear quantile regression averaging (LQRA). Box and Whiskers plots are used to diagnose the overall performance of fitted models.ResultsSingle forecast models (GBM, GAMs, and SVRs) show that including pairwise interactions improves forecast accuracy. The SVR model with interactions based on the radial basis kernel function is the best from single forecast models with the lowest MAE. Combining point forecasts from all the single forecast models using the LQRA approach further reduces the MAE. However, based on the Box and Whiskers plot, the SVR model with pairwise interactions has the smallest range.ConclusionsBased on the key performance indicators, combining predictions from several individual models improves forecast accuracy. However, overall, the SVM with pairwise hierarchical interactions outperforms all the other models

In econometrics and finance, volatility modelling has long been a specialised field for addressin... more In econometrics and finance, volatility modelling has long been a specialised field for addressing a variety of issues pertaining to the risk and uncertainties of an asset. This study presents a robust framework, through a step-by-step design, that is relevant for effective Monte Carlo simulation (MCS) with empirical verifications to estimate volatility using the Generalized Autoregressive Score (GAS) model. The framework describes an organised approach to the MCS experiment that includes "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that consists of writing the code, setting the seed, setting the true parameter a priori, data generation process, and performance assessment through meta-statistics. Among the findings, it is experimentally demonstrated in the study that the GAS model with a lower unconditional shape parameter value can generate a dataset that adeq...

This study rolls out a robust framework relevant for simulation studies through the Generalised A... more This study rolls out a robust framework relevant for simulation studies through the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model using the rugarch package. The package is thoroughly investigated, and novel findings are identified for improved and effective simulations. The focus of the study is to provide necessary simulation steps for volatility estimation that involve "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that includes writing the code, setting the seed, setting the true parameters a priori, data generation process and performance assessment through meta-statistics. This novel, easy-to-understand steps are demonstrated on financial returns using illustrative Monte Carlo simulation with empirical verification. Among the findings, the study shows that regardless of the arrangement of the seed values, the efficiency and consiste...

arXiv (Cornell University), Dec 12, 2022

The increasing demand for electricity and the need for clean energy sources have increased solar ... more The increasing demand for electricity and the need for clean energy sources have increased solar energy use. Accurate forecasts of solar energy are required for easy management of the grid. This paper compares the accuracy of two Gaussian Process Regression (GPR) models combined with Additive Quantile Regression (AQR) and Bayesian Structural Time Series (BSTS) models in the 2-day ahead forecasting of global horizontal irradiance using data from the University of Pretoria from July 2020 to August 2021. Four methods were adopted for variable selection, Lasso, ElasticNet, Boruta, and GBR (Gradient Boosting Regression). The variables selected using GBR were used because they produced the lowest MAE (Minimum Absolute Errors) value. A comparison of seven models GPR (Gaussian Process Regression), Two-layer DGPR (Two-layer Deep Gaussian Process Regression), bstslong (Bayesian Structural Time Series long), AQRA (Additive Quantile Regression Averaging), QRNN(Quantile Regression Neural Network), PLAQR(Partial Linear additive Quantile Regression), and Opera(Online Prediction by ExpRt Aggregation) was made. The evaluation metrics used to select the best model were the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). Further evaluations were done using proper scoring rules and Murphy diagrams. The best individual model was found to be the GPR. The best forecast combination was AQRA ((AQR Averaging) based on MAE. However, based on RMSE, GPNN was the best forecast combination method. Companies such as Eskom could use the methods adopted in this study to control and manage the power grid. The results will promote economic development and sustainability of energy resources.

Climate, Feb 13, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Statistics, Optimization and Information Computing, Jul 28, 2022

Power utility companies rely on forecasting for the operation of electricity demand. This present... more Power utility companies rely on forecasting for the operation of electricity demand. This presents an application of linear quantile regression, non-linear quantile regression, and additive quantile regression models for forecasting extreme electricity demand at peak hours such as 18:00, 19:00, 20:00 and 21:00 using Northern Cape data period 01 January 2000 to 31 March 2014. The variables were selected using the least absolute shrinkage and selection operator. Additive quantile regression models were found to be the best-fitting models for hours 18:00 and 19:00, whereas linear quantile regression models were found to be the best-fitting models for hours 20:00 and 21:00. Out of sample forecasts for seven days (01 to 07 April 2014) were used to solve the unit commitment problem using mixed-integer programming. The unit commitment problem results showed that using all the generating units such as hydroelectric, wind power, concentrated solar power and solar photovoltaic is less costly. This study's main contribution is the development of models for forecasting hourly extreme peak electricity demand. These results could be useful to system operators in the energy sector who have to maintain the minimum cost by scheduling and dispatching electricity during peak hours when the grid is constrained due to peak load demand.

Applied Sciences

Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of th... more Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of the research on solar irradiance forecasting has been based on a single-site analysis. It is crucial to explore multisite modeling to capture variations in weather conditions between various sites, thereby producing a more robust model. In this research, we propose the use of spatial regression coupled with Gaussian Process Regression (GP Spatial) and the GP Autoregressive Spatial model (GP-AR Spatial) for the prediction of GHI using data from seven radiometric stations from South Africa and one from Namibia. The results of the proposed methods were compared with a benchmark model, the Linear Spatial Temporal Regression (LSTR) model. Five validation sets each comprised of three stations were chosen. For each validation set, the remaining five stations were used for training. Based on root mean square error, the GP model gave the most accurate forecasts across the validation sets. These res...

Climate

Extreme value theory is a powerful method that is known to provide statistical models for events ... more Extreme value theory is a powerful method that is known to provide statistical models for events rarely observed. This paper presents a modelling framework for the maximum rainfall data recorded in Limpopo province, South Africa, from 1960 to 2020. Daily and monthly rainfall data were obtained from the South Africa Weather Service. In this work, the r-largest order statistics modelling approach is used. Yearly blocks were used in fitting a 61 years’ data set. The parameters of the developed models were estimated using the maximum likelihood method. After the suitable model for data was chosen, i.e., GEVDr=8, the 50-year return level was estimated as 368 mm, which means a probability of 0.02 exceeding 368 mm in fifty years in the Thabazimbi area. This study helps decision-makers in government and non-profit organisations improve preparation strategies and build resilience in reducing disasters resulting from extreme weather events such as excessive rainfall.

The Southern African Journal of Entrepreneurship and Small Business Management, 2020

This study focused on emerging entrepreneurs operating within the limits of developing economies.... more This study focused on emerging entrepreneurs operating within the limits of developing economies. The framework can be used by emerging entrepreneurs, capacity development institutions and lenders. Methods: A descriptive research design supported by a mixed-method research approach was employed. This was coupled by a two-phase data collection procedure which took place within Limpopo province with 215 participants. Explorative data analysis based on discrete choice models was further implemented. Results: Findings on the EGF illustrated the ability of the framework to act as a more comprehensive diagnostic mechanism that improves early-stage entrepreneurship survival. Conclusion: Entrepreneurship gaps framework is a decision-making tool that can be used by lenders and capacity development institutions to evaluate the emerging entrepreneur with respect to specific areas of business. This results in the necessary support for improving entrepreneur preparedness being provided to entrepreneurs. Secondly, entrepreneurs are likely to benefit from the EGF, if used as a self-diagnostic tool to measure their business preparedness and experience.

System Reliability Management, 2018

Applied Mathematics & Information Sciences, 2019

The paper presents an application of Bayesian structural time-series model to forecast long-term ... more The paper presents an application of Bayesian structural time-series model to forecast long-term electricity demand. Accurate trend specification in long-term forecasting is important; otherwise erroneous forecasts could be obtained especially in South Africa where it is difficult to determine if the trend would continue a downward trajectory or would revert to an upward trajectory. Long-term probabilistic electricity demand forecasts in South Africa from 2013 to 2023 are presented in this paper. The findings are; (a) electricity demand in South Africa is less likely to exceed the highest historical hourly demand of 36 826 kW until 2023 (b) South African demand from Eskom is more likely to maintain the downward trend until 2023 (c) electricity demand lies between 15 849 kW and 39 810 kW with a 90% probability between 2013 and 2023. The contributions of the paper are; (a) application of BSTS to longterm electricity demand forecasting (b) use of autocorrelation plot to determine the number of time lags in long-term electricity demand forecasting (c) long-term forecasting of electricity demand using South African data with their uncertainties quantified.

Energies, Jan 13, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Journal of Risk and Financial Management

This study rolls out a robust framework relevant for simulation studies through the Generalised A... more This study rolls out a robust framework relevant for simulation studies through the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model using the rugarch package. The package is thoroughly investigated, and novel findings are identified for improved and effective simulations. The focus of the study is to provide necessary simulation steps to determine appropriate distributions of innovations relevant for estimating the persistence of volatility. The simulation steps involve “background (optional), defining the aim, research questions, method of implementation, and summarised conclusion”. The method of implementation is a workflow that includes writing the code, setting the seed, setting the true parameters a priori, data generation process and performance assessment through meta-statistics. These novel, easy-to-understand steps are demonstrated on financial returns using illustrative Monte Carlo simulation with empirical verification. Among the findings, the stud...

African Journal of Business Management, 2012

The paper investigates the impact of day of the week, holidays and other seasonal effects on dail... more The paper investigates the impact of day of the week, holidays and other seasonal effects on daily electricity demand in South Africa using regression, seasonal autoregressive integrated moving average (SARIMA) and regression model with SARIMA (RegSARIMA) models for the period 2001 to 2009. The results from this study show that the SARIMA model produces better forecast accuracy with a mean absolute percent error (MAPE) of 1.36%. The RegSARIMA model had a MAPE of 1.75%. From these model results, we can conclude that, holidays play a major role in determining the demand of electricity.

AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 2012

The paper discusses the modelling and forecasting of daily winter peak electricity loads in South... more The paper discusses the modelling and forecasting of daily winter peak electricity loads in South Africa for the period 2000 to 2009 using a regression model that allows for nonlinear and nonparametric terms.A demand model for daily winter peaks is developed and used for out of sample predictions. Empirical results show that electricity demand in South Africa is highly sensitive to temperature fluctuations during the winter periods.

http://scholar.google.com/citations?user=OKgPPJAAAAAJ&hl=en

African Journal of Science, Technology, Innovation and Development, Dec 1, 2020

In the early stage of business, which is where most new ventures fail, many entrepreneurs experie... more In the early stage of business, which is where most new ventures fail, many entrepreneurs experience discrepancies between their entrepreneurial expectations and business realities. These discrepancies referred to by this paper as an entrepreneurial gap (EG) are, therefore, among other factors, professed to be responsible for the high attrition rate of emerging ventures in South Africa. An oversight in this area of EG, despite the provision of most required resources, may still lead to business failure. This paper argues that there is more yet to be comprehended regarding early-stage business success, concerning the entrepreneur component. The purpose of this paper was to recognize and classify factors responsible for establishing entrepreneurial gaps with the intent to improve the level of preparedness among emerging entrepreneurs. A qualitative approach with in-depth interviews was employed in the data collection. ATLAS ti 8 was used to unpack factors that instigate entrepreneurial gaps while posing challenges to emerging entrepreneurs in the early stage of business. The groups identified were: entrepreneur management, familism and personal management. The findings provide information that is credible to improving the level of preparedness among emerging entrepreneurs, and could be used by mentors, coaches and relevant support structures.

American Journal of Infection Control, Mar 1, 2023

BackgroundThis study aims to show that including pairwise hierarchical interactions of covariates... more BackgroundThis study aims to show that including pairwise hierarchical interactions of covariates and combining forecasts from individual models improves prediction accuracy.MethodsThe least absolute shrinkage and selection operator via hierarchical pairwise interaction is used in selecting variables that are not correlated and with the greatest predictive power in single forecast models (Gradient boosting method [GBM], Generalized additive models [GAMs], Support vector regression [SVR]) are used in the analysis. The best model was selected based on the mean absolute error (MAE), the best key performance indicator for skewed data. Forecasts from the 5 models were combined using linear quantile regression averaging (LQRA). Box and Whiskers plots are used to diagnose the overall performance of fitted models.ResultsSingle forecast models (GBM, GAMs, and SVRs) show that including pairwise interactions improves forecast accuracy. The SVR model with interactions based on the radial basis kernel function is the best from single forecast models with the lowest MAE. Combining point forecasts from all the single forecast models using the LQRA approach further reduces the MAE. However, based on the Box and Whiskers plot, the SVR model with pairwise interactions has the smallest range.ConclusionsBased on the key performance indicators, combining predictions from several individual models improves forecast accuracy. However, overall, the SVM with pairwise hierarchical interactions outperforms all the other models

In econometrics and finance, volatility modelling has long been a specialised field for addressin... more In econometrics and finance, volatility modelling has long been a specialised field for addressing a variety of issues pertaining to the risk and uncertainties of an asset. This study presents a robust framework, through a step-by-step design, that is relevant for effective Monte Carlo simulation (MCS) with empirical verifications to estimate volatility using the Generalized Autoregressive Score (GAS) model. The framework describes an organised approach to the MCS experiment that includes "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that consists of writing the code, setting the seed, setting the true parameter a priori, data generation process, and performance assessment through meta-statistics. Among the findings, it is experimentally demonstrated in the study that the GAS model with a lower unconditional shape parameter value can generate a dataset that adeq...

This study rolls out a robust framework relevant for simulation studies through the Generalised A... more This study rolls out a robust framework relevant for simulation studies through the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model using the rugarch package. The package is thoroughly investigated, and novel findings are identified for improved and effective simulations. The focus of the study is to provide necessary simulation steps for volatility estimation that involve "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that includes writing the code, setting the seed, setting the true parameters a priori, data generation process and performance assessment through meta-statistics. This novel, easy-to-understand steps are demonstrated on financial returns using illustrative Monte Carlo simulation with empirical verification. Among the findings, the study shows that regardless of the arrangement of the seed values, the efficiency and consiste...

arXiv (Cornell University), Dec 12, 2022

The increasing demand for electricity and the need for clean energy sources have increased solar ... more The increasing demand for electricity and the need for clean energy sources have increased solar energy use. Accurate forecasts of solar energy are required for easy management of the grid. This paper compares the accuracy of two Gaussian Process Regression (GPR) models combined with Additive Quantile Regression (AQR) and Bayesian Structural Time Series (BSTS) models in the 2-day ahead forecasting of global horizontal irradiance using data from the University of Pretoria from July 2020 to August 2021. Four methods were adopted for variable selection, Lasso, ElasticNet, Boruta, and GBR (Gradient Boosting Regression). The variables selected using GBR were used because they produced the lowest MAE (Minimum Absolute Errors) value. A comparison of seven models GPR (Gaussian Process Regression), Two-layer DGPR (Two-layer Deep Gaussian Process Regression), bstslong (Bayesian Structural Time Series long), AQRA (Additive Quantile Regression Averaging), QRNN(Quantile Regression Neural Network), PLAQR(Partial Linear additive Quantile Regression), and Opera(Online Prediction by ExpRt Aggregation) was made. The evaluation metrics used to select the best model were the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). Further evaluations were done using proper scoring rules and Murphy diagrams. The best individual model was found to be the GPR. The best forecast combination was AQRA ((AQR Averaging) based on MAE. However, based on RMSE, GPNN was the best forecast combination method. Companies such as Eskom could use the methods adopted in this study to control and manage the power grid. The results will promote economic development and sustainability of energy resources.

Climate, Feb 13, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Statistics, Optimization and Information Computing, Jul 28, 2022

Power utility companies rely on forecasting for the operation of electricity demand. This present... more Power utility companies rely on forecasting for the operation of electricity demand. This presents an application of linear quantile regression, non-linear quantile regression, and additive quantile regression models for forecasting extreme electricity demand at peak hours such as 18:00, 19:00, 20:00 and 21:00 using Northern Cape data period 01 January 2000 to 31 March 2014. The variables were selected using the least absolute shrinkage and selection operator. Additive quantile regression models were found to be the best-fitting models for hours 18:00 and 19:00, whereas linear quantile regression models were found to be the best-fitting models for hours 20:00 and 21:00. Out of sample forecasts for seven days (01 to 07 April 2014) were used to solve the unit commitment problem using mixed-integer programming. The unit commitment problem results showed that using all the generating units such as hydroelectric, wind power, concentrated solar power and solar photovoltaic is less costly. This study's main contribution is the development of models for forecasting hourly extreme peak electricity demand. These results could be useful to system operators in the energy sector who have to maintain the minimum cost by scheduling and dispatching electricity during peak hours when the grid is constrained due to peak load demand.

Applied Sciences

Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of th... more Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of the research on solar irradiance forecasting has been based on a single-site analysis. It is crucial to explore multisite modeling to capture variations in weather conditions between various sites, thereby producing a more robust model. In this research, we propose the use of spatial regression coupled with Gaussian Process Regression (GP Spatial) and the GP Autoregressive Spatial model (GP-AR Spatial) for the prediction of GHI using data from seven radiometric stations from South Africa and one from Namibia. The results of the proposed methods were compared with a benchmark model, the Linear Spatial Temporal Regression (LSTR) model. Five validation sets each comprised of three stations were chosen. For each validation set, the remaining five stations were used for training. Based on root mean square error, the GP model gave the most accurate forecasts across the validation sets. These res...

Climate

Extreme value theory is a powerful method that is known to provide statistical models for events ... more Extreme value theory is a powerful method that is known to provide statistical models for events rarely observed. This paper presents a modelling framework for the maximum rainfall data recorded in Limpopo province, South Africa, from 1960 to 2020. Daily and monthly rainfall data were obtained from the South Africa Weather Service. In this work, the r-largest order statistics modelling approach is used. Yearly blocks were used in fitting a 61 years’ data set. The parameters of the developed models were estimated using the maximum likelihood method. After the suitable model for data was chosen, i.e., GEVDr=8, the 50-year return level was estimated as 368 mm, which means a probability of 0.02 exceeding 368 mm in fifty years in the Thabazimbi area. This study helps decision-makers in government and non-profit organisations improve preparation strategies and build resilience in reducing disasters resulting from extreme weather events such as excessive rainfall.

The Southern African Journal of Entrepreneurship and Small Business Management, 2020

This study focused on emerging entrepreneurs operating within the limits of developing economies.... more This study focused on emerging entrepreneurs operating within the limits of developing economies. The framework can be used by emerging entrepreneurs, capacity development institutions and lenders. Methods: A descriptive research design supported by a mixed-method research approach was employed. This was coupled by a two-phase data collection procedure which took place within Limpopo province with 215 participants. Explorative data analysis based on discrete choice models was further implemented. Results: Findings on the EGF illustrated the ability of the framework to act as a more comprehensive diagnostic mechanism that improves early-stage entrepreneurship survival. Conclusion: Entrepreneurship gaps framework is a decision-making tool that can be used by lenders and capacity development institutions to evaluate the emerging entrepreneur with respect to specific areas of business. This results in the necessary support for improving entrepreneur preparedness being provided to entrepreneurs. Secondly, entrepreneurs are likely to benefit from the EGF, if used as a self-diagnostic tool to measure their business preparedness and experience.

System Reliability Management, 2018

Applied Mathematics & Information Sciences, 2019

The paper presents an application of Bayesian structural time-series model to forecast long-term ... more The paper presents an application of Bayesian structural time-series model to forecast long-term electricity demand. Accurate trend specification in long-term forecasting is important; otherwise erroneous forecasts could be obtained especially in South Africa where it is difficult to determine if the trend would continue a downward trajectory or would revert to an upward trajectory. Long-term probabilistic electricity demand forecasts in South Africa from 2013 to 2023 are presented in this paper. The findings are; (a) electricity demand in South Africa is less likely to exceed the highest historical hourly demand of 36 826 kW until 2023 (b) South African demand from Eskom is more likely to maintain the downward trend until 2023 (c) electricity demand lies between 15 849 kW and 39 810 kW with a 90% probability between 2013 and 2023. The contributions of the paper are; (a) application of BSTS to longterm electricity demand forecasting (b) use of autocorrelation plot to determine the number of time lags in long-term electricity demand forecasting (c) long-term forecasting of electricity demand using South African data with their uncertainties quantified.

Energies, Jan 13, 2023

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Journal of Risk and Financial Management

This study rolls out a robust framework relevant for simulation studies through the Generalised A... more This study rolls out a robust framework relevant for simulation studies through the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model using the rugarch package. The package is thoroughly investigated, and novel findings are identified for improved and effective simulations. The focus of the study is to provide necessary simulation steps to determine appropriate distributions of innovations relevant for estimating the persistence of volatility. The simulation steps involve “background (optional), defining the aim, research questions, method of implementation, and summarised conclusion”. The method of implementation is a workflow that includes writing the code, setting the seed, setting the true parameters a priori, data generation process and performance assessment through meta-statistics. These novel, easy-to-understand steps are demonstrated on financial returns using illustrative Monte Carlo simulation with empirical verification. Among the findings, the stud...

African Journal of Business Management, 2012

The paper investigates the impact of day of the week, holidays and other seasonal effects on dail... more The paper investigates the impact of day of the week, holidays and other seasonal effects on daily electricity demand in South Africa using regression, seasonal autoregressive integrated moving average (SARIMA) and regression model with SARIMA (RegSARIMA) models for the period 2001 to 2009. The results from this study show that the SARIMA model produces better forecast accuracy with a mean absolute percent error (MAPE) of 1.36%. The RegSARIMA model had a MAPE of 1.75%. From these model results, we can conclude that, holidays play a major role in determining the demand of electricity.

AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 2012

The paper discusses the modelling and forecasting of daily winter peak electricity loads in South... more The paper discusses the modelling and forecasting of daily winter peak electricity loads in South Africa for the period 2000 to 2009 using a regression model that allows for nonlinear and nonparametric terms.A demand model for daily winter peaks is developed and used for out of sample predictions. Empirical results show that electricity demand in South Africa is highly sensitive to temperature fluctuations during the winter periods.