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Modelling altered revenue function based on varying power consumption distribution and electricity tariff charge using data analytics framework.

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dc.ConferencecodeUniv Malaysia Pahang, Fac Ind Sci & Technol, Inst Teknologi Sepuluh, Soc Ind & Appl Math, Malaysian Math Sci Soc, Malaysian Inst Stat
dc.ConferencedateAUG 08-10, 2017
dc.ConferencelocationKuantan, MALAYSIA
dc.Conferencename1st International Conference on Applied and Industrial Mathematics and Statistics (ICoAIMS)
dc.contributor.authorZainudin, WNRAen_US
dc.contributor.authorRamli, NAen_US
dc.date.accessioned2024-05-29T03:27:04Z
dc.date.available2024-05-29T03:27:04Z
dc.date.issued2017
dc.description.abstractIn 2010, Energy Commission (EC) had introduced Incentive Based Regulation (IBR) to ensure sustainable Malaysian Electricity Supply Industry (MESI), promotes transparent and fair returns, encourage maximum efficiency and maintains policy driven end user tariff. To cater such revolutionary transformation, a sophisticated system to generate policy driven electricity tariff structure is in great need. Hence, this study presents a data analytics framework that generates altered revenue function based on varying power consumption distribution and tariff charge function. For the purpose of this study, the power consumption distribution is being proxy using proportion of household consumption and electricity consumed in KwH and the tariff charge function is being proxy using three-tiered increasing block tariff (IBT). The altered revenue function is useful to give an indication on whether any changes in the power consumption distribution and tariff charges will give positive or negative impact to the economy. The methodology used for this framework begins by defining the revenue to be a function of power consumption distribution and tariff charge function. Then, the proportion of household consumption and tariff charge function is derived within certain interval of electricity power. Any changes in those proportion are conjectured to contribute towards changes in revenue function. Thus, these changes can potentially give an indication on whether the changes in power consumption distribution and tariff charge function are giving positive or negative impact on TNB revenue. Based on the finding of this study, major changes on tariff charge function seems to affect altered revenue function more than power consumption distribution. However, the paper concludes that power consumption distribution and tariff charge function can influence TNB revenue to some great extent.
dc.identifier.doi10.1088/742-6596/890/1/012099
dc.identifier.issn1742-6588
dc.identifier.scopusWOS:000423857800099
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/12182
dc.identifier.volume890
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherIOP PUBLISHING LTDen_US
dc.sourceWeb Of Science (ISI)
dc.sourcetitle1ST INTERNATIONAL CONFERENCE ON APPLIED & INDUSTRIAL MATHEMATICS AND STATISTICS 2017 (ICOAIMS 2017)
dc.titleModelling altered revenue function based on varying power consumption distribution and electricity tariff charge using data analytics framework.en_US
dspace.entity.typePublication

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