Следене
Paul Mokilane
Paul Mokilane
Други именаPaul Moloantoa Mokilane
Statistician/data scientist, CSIR
Потвърден имейл адрес: CSIR.co.za
Заглавие
Позовавания
Позовавания
Година
Density forecasting for long-term electricity demand in South Africa using quantile regression
P Mokilane
South African Journal of Economic and Management Sciences 21 (1), 1-14, 2018
222018
Bayesian structural time-series approach to a long-term electricity demand forecasting
P Mokilane, P Debba, VSS Yadavalli, C Sigauke
Natural Sciences Publishing, 2019
82019
Forecasts for Electricity Demand in South Africa 2014–2050 Using the CSIR Sectoral Regression Model
JP Holloway, P Mokilane, S Makhany, T Magadla, R Koen
Project Report Eskom, 2016
72016
Developing long-term scenario forecasts to support electricity generation investment decisions
R Koen, T Magadla, P Mokilane
43rd Annual Conference of the Operations Research Society of South Africa, 9, 2014
72014
A Rasch analysis to determine the difficulty of the National Senior Certificate Mathematics examination
J Sewry, P Mokilane
Perspectives in Education 32 (1), 192-209, 2014
32014
A comparative study of over-sampling techniques as applied to seismic events
M Mokoatle, T Coleman, P Mokilane
Southern African Conference for Artificial Intelligence Research, 331-345, 2023
22023
Exploring the usefulness of the INLA model in predicting levels of crime in the City of Johannesburg, South Africa
T Coleman, P Mokilane, M Rangata, J Holloway, N Botha, R Koen, ...
Crime Science 13 (1), 25, 2024
12024
Probabilistic long-term electricity demand forecasting in South Africa
PM Mokilane
2019
Long-term electricity demand forecasting using a generalised additive mixed quantile averaging (GAMMQV)
PM Mokilane, P Debba, VSS Yadavalli, C Sigauke
IEOM Society International, 2018
2018
The application and empirical comparison of item parameters of Classical Test Theory and Partial Credit Model of Rasch in performance assessments
PM Mokilane
University of South Africa, 2014
2014
Long-term electricity demand forecasting using a generalised additive mixed quantile averaging (GAMMQV) model
P Mokilane, P Debba, VSS Yadavalli, C Sigauke
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