{"title":"Modelling NO2 emissions from Eskom’s coal fired power stations using Generalised Linear Models","authors":"Delson Chikobvu, Mpendulo Mamba","doi":"10.17159/2413-3051/2022/v33i4a13819","DOIUrl":null,"url":null,"abstract":"The 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.","PeriodicalId":15666,"journal":{"name":"Journal of Energy in Southern Africa","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Energy in Southern Africa","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17159/2413-3051/2022/v33i4a13819","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
The 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.
期刊介绍:
The journal has a regional focus on southern Africa. Manuscripts that are accepted for consideration to publish in the journal must address energy issues in southern Africa or have a clear component relevant to southern Africa, including research that was set-up or designed in the region. The southern African region is considered to be constituted by the following fifteen (15) countries: Angola, Botswana, Democratic Republic of Congo, Lesotho, Malawi, Madagascar, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe.
Within this broad field of energy research, topics of particular interest include energy efficiency, modelling, renewable energy, poverty, sustainable development, climate change mitigation, energy security, energy policy, energy governance, markets, technology and innovation.