Delson Chikobvu

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

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.

Peak electricity demand forecasting using time series regression models: An application to South African data

Abstract Forecasting of electricity demand requires the use of models which capture important drivers of demand. In this paper we use two time series regression (TSR) models for short term forecasting. South African hourly electricity data for the period, years 2000 to 2010 is used. The first TSR model is one in which the temperature effects are captured through heating and cooling degree days. We refer to this as TSR model 1. In the second TSR model the temperature effects are captured though regression splines. This is TSR model 2. The third model includes a component which captures the volatility in electricity demand. A comparative analysis is done with the first two models in out of sample predictions of up to four weeks. Empirical results show that the model in which temperature is incorporated through regression splines produces better forecasts. An analysis of under demand predictions is done by comparing model 3 with model 2. Results show that both models, i.e. 2 and 3 respectively are comparable. However model 3 seem to capture well the volatility in the residuals during the year 2008 when we experienced a world recession.

Analysis of the same day of the week increases in peak electricity demand in South Africa

Modelling of the same day of the week increases in peak electricity demand using the Generalized Pareto-type (GP-type) distribution is discussed. The GP-type distribution discussed in this paper has one parameter to estimate and as such, it is referred to as the Generalized Single Pareto (GSP). The data is from Eskom, South Africas power utility company and is for the years 2000 to 2011. A comparative analysis is done with a Generalized Pareto Distribution (GPD). Although both the GSP and the GPD fit the data, the use of the GSP is easier since it has only one parameter to estimate instead of two as is the case with the GPD. Modelling of the same day of the week increases in peak electricity demand improves the reliability of a power network if an accurate assessment of the level and frequency of future extreme load forecasts is carried out.

Time-homogeneous Markov process for HIV/AIDS progression under a combination treatment therapy: cohort study, South Africa

BackgroundAs HIV enters the human body, its main target is the CD4 cell which it turns into a factory that produces millions of other HIV particles. These HIV particles target new CD4 cells resulting in the progression of HIV infection to AIDS. A continuous depletion of CD4 cells results in opportunistic infections, for example tuberculosis (TB). The purpose of this study is to model and describe the progression of HIV/AIDS disease in an individual on antiretroviral therapy (ART) follow up using a continuous time homogeneous Markov process. A cohort of 319 HIV infected patients on ART follow up at a Wellness Clinic in Bela Bela, South Africa is used in this study. Though Markov models based on CD4 cell counts is a common approach in HIV/AIDS modelling, this paper is unique clinically in that tuberculosis (TB) co-infection is included as a covariate.MethodsThe method partitions the HIV infection period into five CD4-cell count intervals followed by the end points; death, and withdrawal from study. The effectiveness of treatment is analysed by comparing the forward transitions with the backward transitions. The effects of reaction to treatment, TB co-infection, gender and age on the transition rates are also examined. The developed models give very good fit to the data.ResultsThe results show that the strongest predictor of transition from a state of CD4 cell count greater than 750 to a state of CD4 between 500 and 750 is a negative reaction to drug therapy. Development of TB during the course of treatment is the greatest predictor of transitions to states of lower CD4 cell count. Transitions from good states to bad states are higher on male patients than their female counterparts. Patients in the cohort spend a greater proportion of their total follow-up time in higher CD4 states.ConclusionFrom some of these findings we conclude that there is need to monitor adverse reaction to drugs more frequently, screen HIV/AIDS patients for any signs and symptoms of TB and check for factors that may explain gender differences further.

Determinants of viral load rebound on HIV/AIDS patients receiving antiretroviral therapy: results from South Africa

BackgroundAntiretroviral therapy (ART) has become the standard of care for patients with HIV infection in South Africa and has led to the reduction in AIDS related morbidity and mortality. In developing countries, the nucleosides reverse transcriptase inhibitors (NRTIs) class are widely used because of their low production costs. However patients treated with NRTIs develop varying degree of toxicity after long-term therapy. For this study patients are administered with a triple therapy of two NRTIs and one non-nucleoside reverse transcriptase inhibitor (NNRTI).MethodIn this study the progression of HIV in vivo is divided into some viral load states and a continuous time-homogeneous model is fitted to assess the effects of covariates namely gender, age, CD4 baseline, viral load baseline, lactic acidosis, peripheral neuropathy, non-adherence and resistance to treatment on transition intensities between the states. Effects of different drug combinations on transition intensities are also assessed.ResultsThe results show no gender differences on transition intensities. The likelihood ratio test shows that the continuous time Markov model for the effects of the covariates including combination give a significantly better fit to the observed data. From almost all states, rates of viral suppression were higher than rates of viral rebound except for patients in state 2 (viral load between 50 and 10,000 copies/mL) where rates of viral rebound to state 3 (viral load between 10,000 and 100,000 copies/mL) were higher than rates of viral suppression to undetectable levels. For this transition, confidence intervals were very small. This was quite notable for patients who were administered with AZT-3TC-LPV/r and FTC-TDF-EFV. Although patients on d4T-3TC-EFV also had higher rates of viral rebound from state 2 than suppression, the difference was not significant.ConclusionFrom these findings, we can conclude that administering of any HIV drug regimen is better when based on the viral load level of an HIV+ patient. Before initiation of treatment, patients should be well equipped on how antiretroviral drugs operate including possibilities of toxicity in order to reduce chances of non-adherence to treatment. There should also be a good relationship between patient and health-care-giver to ensure proper adherence to treatment. Uptake of therapy by young patients should be closely monitored by adopting pill counting every time they come for review.