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

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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
Number of pages6
ISBN (Print)978-1-5090-0620-5
Publication statusPublished - 2016
EventIJCNN 2016: International Joint Conference on Neural Networks - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016


ConferenceIJCNN 2016


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

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