Women and human development: the capabilities approach

Environmental Problems in an Urbanizing World

https://gspp.berkeley.edu/assets/uploads/research/pdf/Bazilian_et_al_2012_Energy_Access_Scenarios_to_2030.pdf

Energy access scenarios to 2030 for the power sector in sub-Saharan Africa

Short-term residential load forecasting: Impact of calendar effects and forecast granularity

Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

Prediction of daily peak electricity demand in South Africa using volatility forecasting models

CD4 cell count has been identified to be an essential component in monitoring HIV treatment outcome. However, CD4 cell count monitoring sometimes fails to predict virological failure resulting in unnecessary switch of treatment lines which causes drug resistance and limitations of treatment options. This study assesses the use of both viral load (HIV RNA) and CD4 cell count in the monitoring of HIV/AIDS progression. Time-homogeneous Markov models were fitted, one on CD4 cell count monitoring and the other on HIV RNA monitoring. Effects of covariates; gender, age, CD4 baseline, HIV RNA baseline and adherence to treatment were assessed for each of the fitted models. Assessment of the fitted models was done using prevalence plots and the likelihood ratio tests. The analysis was done using the “msm” package in R. Results from the analysis show that viral load monitoring predicts deaths of HIV/AIDS patients better than CD4 cell count monitoring. Assessment of the fitted models shows that viral load monitoring is a better predictor of HIV/AIDS progression than CD4 cell count. From this study one can conclude that although patients take more time to achieve a normal CD4 cell count and less time to achieve an undetectable viral load, once the CD4 cell count is normal, mortality risks are reduced. Therefore, both viral load monitoring and CD4 count monitoring can be used to provide useful information which can be used to improve life expectance of patients living with HIV. However, viral load monitoring is a better predictor of HIV/AIDS progression than CD4 cell count and hence viral load is deemed superior.

/pdf/a-superiority-of-viral-load-over-cd4-cell-count-when-2d6cp72fxk.pdf

A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy

In this paper, seasonal autoregressive integrated moving average (SARIMA) and regression with SARIMA errors (regression-SARIMA) models are developed to predict daily peak electricity demand in South Africa using data for the period 1996 to 2009. The performance of the developed models is evaluated by comparing them with Winter’s triple exponential smoothing model. Empirical results from the study show that the SARIMA model produces more accurate short-term forecasts. The regression-SARIMA modelling framework captures important drivers of electricity demand. These results are important to decision makers, load forecasters and systems operators in load flow analysis and scheduling of electricity.

/pdf/regression-sarima-modelling-of-daily-peak-electricity-demand-1wx79fxa1f.pdf

Regression-SARIMA modelling of daily peak electricity demand in South Africa

Accurate prediction of daily peak load demand is very important for decision makers in the energy sector. This helps in the determination of consistent and reliable supply schedules during peak periods. Accurate short term load forecasts enable effective load shifting between transmission substations, scheduling of startup times of peak stations, load flow analysis and power system security studies. A multivariate adaptive regression splines (MARS) modelling approach towards daily peak electricity load forecasting in South Africa is presented in this paper for the period 2000 to 2009. MARS is a non-parametric multivariate regression method which is used in high-dimensional problems with complex model structures, such as nonlinearities, interactions and missing data, in a straight forward manner and produces results which may easily be explained to management. The models developed in this paper consist of components that represent calendar and meteorological data. The performances of the models are evaluated by comparing them to a piecewise linear regression model. The results from the study show that the MARS models achieve better forecast accuracy.

/pdf/daily-peak-electricity-load-forecasting-in-south-africa-1bp23z0ym0.pdf

Daily peak electricity load forecasting in South Africa using a multivariate non-parametric regression approach

The paper discusses the modelling of the influence of temperature on average daily electricity demand in South Africa using a piecewise linear regression model and the generalized extreme value theory approach for the period - 2000 to 2010. Empirical results show that electricity demand in South Africa is highly sensitive to cold temperatures. Extreme low average daily temperatures of the order of 8.20C are very rare in South Africa. They only occur about 8 times in a year and result in huge increases in electricity demand.

http://www.scielo.org.za/pdf/jesa/v24n4/08.pdf

Delson Chikobvu

Papers

Prediction of daily peak electricity demand in South Africa using volatility forecasting models

A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy

Regression-SARIMA modelling of daily peak electricity demand in South Africa

Daily peak electricity load forecasting in South Africa using a multivariate non-parametric regression approach

Modelling influence of temperature on daily peak electricity demand in South Africa