Neural Network-based Load Forecasting and Error Implication for Short-term Horizon

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)
67 Downloads (Pure)

Abstract

Load forecasting is considered vital along with many other important entities required for assessing the reliability of power system. Thus, the primary concern is not to forecast load with a novel model, rather to forecast load with the highest accuracy. Short-term load forecast accuracy is often hindered due to various load impacting factors. Two of the major impacting factors are day-ahead weather forecast and subsequent variation in electricity demand that is independent of weather. To tackle the uncertainty in short-term load forecasting, this paper presents a neural network-based load forecasting technique for short-term horizon based on data corresponding to a U.S. independent system operator. With the real life data, a better understanding of forecasting error is carried out while further identifying the time periods when the load is supposedly to be over- or under-forecast.
Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages4970-4975
Number of pages6
ISBN (Print)978-1-5090-0620-5
DOIs
Publication statusPublished - 2016
EventIJCNN 2016: International Joint Conference on Neural Networks - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Conference

ConferenceIJCNN 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

Keywords

  • Error analysis
  • forecasting
  • forecast error
  • load forecast uncertainty
  • neural network
  • short-term load forecast

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