Norman Maswanganyi - Academia.edu (original) (raw)
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Papers by Norman Maswanganyi
Energies, 2021
It is important to predict extreme electricity demand in power utilities as the uncertainties in ... more It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insig...
AIMS Energy
Forecasting electricity demand in South Africa remains an increasingly national challenge as the ... more Forecasting electricity demand in South Africa remains an increasingly national challenge as the government does not sufficiently take into account the impact of the electricity prices in their electricity demand forecast. Effective measures to rapidly reduce the demand of electricity are urgently needed to deal with future electricity prices and government policies uncertainties within the energy industry. Moreover, long-term peak electricity demand forecasting methods are needed to quantify the uncertainty of future electricity demand for better electricity security management. The prediction of long-term electricity demand assists decision makers in the electricity sector in planning for capacity generation. This paper presents an application of quantile regression averaging (QRA) approach using South African monthly and quarterly data ranging from January 2007 to December 2014. Variable selection is done in a comparative manner using ridge, least absolute shrinkage and selection operator (Lasso), cross validation (CV) and elastic net. We compare the forecasting accuracy of monthly peak electricity demand (MPED) and quarterly peak electricity demand (QPED) forecasting models using generalised additive models (GAMs) and QRA. The coefficient estimates for ridge, Lasso and elastic net are estimated and compared using MPED and QPED data.
Energies, 2021
It is important to predict extreme electricity demand in power utilities as the uncertainties in ... more It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insig...
AIMS Energy
Forecasting electricity demand in South Africa remains an increasingly national challenge as the ... more Forecasting electricity demand in South Africa remains an increasingly national challenge as the government does not sufficiently take into account the impact of the electricity prices in their electricity demand forecast. Effective measures to rapidly reduce the demand of electricity are urgently needed to deal with future electricity prices and government policies uncertainties within the energy industry. Moreover, long-term peak electricity demand forecasting methods are needed to quantify the uncertainty of future electricity demand for better electricity security management. The prediction of long-term electricity demand assists decision makers in the electricity sector in planning for capacity generation. This paper presents an application of quantile regression averaging (QRA) approach using South African monthly and quarterly data ranging from January 2007 to December 2014. Variable selection is done in a comparative manner using ridge, least absolute shrinkage and selection operator (Lasso), cross validation (CV) and elastic net. We compare the forecasting accuracy of monthly peak electricity demand (MPED) and quarterly peak electricity demand (QPED) forecasting models using generalised additive models (GAMs) and QRA. The coefficient estimates for ridge, Lasso and elastic net are estimated and compared using MPED and QPED data.