2 research outputs found
Modelling NO2 emissions from Eskom’s coal fired power stations using Generalised Linear Models
No full textThe aim of this paper is to determine if a Generalised Linear Model (GLM) is a better model over the traditional simple linear regression when fitted to nitrogen dioxide (NO2) emitted into the atmosphere during the production of electricity from 13 Eskom’s coal fuelled power stations. A GLM was fitted to the NO2 emission data using forward and backward selection of variables for the models. A similar model using regression analysis was fitted for comparison. The results show that a GLM can be used to predict and explain NO2 emissions from coal fired electricity stations in South Africa. The Lognormal model was found to be the better model by diagnostic measures including plots that showed improved variance behaviour in the residuals. Various variables such as amount of electricity sent out (in GWhs), age of power station (in years), power station used, and interaction terms such as electricity and station, Age and station can be used in describing/ predicting NO2 emissions (in tons) from Eskom’s coal fuelled power stations
A Loggamma Generalised Linear Model for NO<sub>2</sub> Emissions Data from South Africa’s Eskom’s Coal-Fired Power Stations When the Data Are Non-Normal and the Variance Is Non-Constant
No full textThe aim of this paper is to determine if the Loggamma distribution model in a Generalised Linear Model (GLM) setup is a better model than the traditional simple linear regression model and the Lognormal-based GLM when fitted to nitrogen dioxide (NO2) emissions data generated during the production of electricity from 13 Eskom’s coal-fuelled power stations in South Africa. The variables explaining the NO2 emissions data are selected using backward stepwise variable selection techniques. The variables considered include the power station itself, the amount of electricity generated from the power station, the age in years of the power station, the abatement technology (filter) used at the particular power station, and the month of the year. Interaction terms between the variables are also considered. The maximum likelihood estimation (MLE) method is used to estimate parameters of the GLM, and ordinary least squares is used to estimate parameters for the regression model. The Normal, Lognormal, and Loggamma distribution models with identity link function are fitted to the NO2 emissions data. The variance of the NO2 emissions increases with mean emissions and the Loggamma model plots, and the explained variance metrics (the variance-function-based R2 and adjusted R2) confirm the best fit to the data over the Normally distributed regression model and Lognormal-based GLM. Thus, NO2 emissions at Eskom in South Africa can be explained and predicted by employing the Loggamma-based GLM model. The findings will assist in providing information for the development of effective strategies for mitigating air pollution and promoting sustainable practices within the energy sector in South Africa
