Approach for forecasting smart customer demand with significant energy demand variability

Zafar Khan, Dilan Jayaweera

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
298 Downloads (Pure)


Load forecasting in an emerging smart grid has become a challenging task. This paper presents an innovative approach to forecast highly variable smart customer load using smart meter energy consumption data. The smart meter data is systematically linearized by applying extended k-means clustering approach, smoothing the linearized load profiles and then linearizing the load profiles using Taylor series linearization process. Case studies are presented using real world smart meter data and then applying the proposed approach and artificial neural network. Four different scenarios are considered for forecasting and the results showed that, in case of high variability in smart customer energy demand, the accuracy of forecasting using linearized profiles is higher than using original non-linear profiles as the source of forecasting. The forecasting process was repeated several times to verify the robustness of the approach and the results justify the accuracy of the forecast further with the proposed approach.
Original languageEnglish
Title of host publication2018 1st International Conference on Power, Energy and Smart Grid (ICPESG)
PublisherIEEE Xplore
Number of pages5
ISBN (Electronic)9781538654828
ISBN (Print)9781538654835 (PoD)
Publication statusPublished - 14 Jun 2018
EventInternational Conference on Power, Energy and Smart Grid - Mirpur University of Science and Technology (MUST), Pakistan
Duration: 9 Apr 201810 Apr 2018


ConferenceInternational Conference on Power, Energy and Smart Grid
Abbreviated titleICPESG-2018
Internet address


  • Artificial neural network
  • Load forecasting
  • Smart grid
  • Smart meter


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