A composite k-nearest neighbor model for day-ahead load forecasting with limited temperature forecasts

Rui Zhang, Yan Xu, Zhao Yang Dong, Weicong Kong, Kit Po Wong

    Research output: Chapter in Book/Conference paperConference paper

    11 Citations (Scopus)

    Abstract

    Load forecasting is an important task in power system operations. Considering the strong correlation between electricity load demand and weather condition, the temperature has always been an input for short-term load forecasting. For day-ahead load forecasting, the whole next-day's temperature forecast (say, hourly or half-hourly forecast) is however sometimes difficult to obtain or suffering from uncertain forecasting errors. This paper proposes a k-nearest neighbor (kNN)-based model for predicting the next-day's load with only limited temperature forecasts, namely minimum and maximum temperature of a day, as the forecasting input. The proposed model consists of three individual kNN models which have different neighboring rules. The three are combined together by tuned weighting factors for a final forecasting output. The proposed model is tested on the Australian National Electricity Market (NEM) data, showing reasonably high accuracy. It can be used as an alternative tool for day-ahead load forecasting when only limited temperature information is available.

    Original languageEnglish
    Title of host publication2016 IEEE Power and Energy Society General Meeting
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    ISBN (Electronic)9781509041688
    DOIs
    Publication statusPublished - 10 Nov 2016
    Event2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States
    Duration: 17 Jul 201621 Jul 2016

    Conference

    Conference2016 IEEE Power and Energy Society General Meeting, PESGM 2016
    CountryUnited States
    CityBoston
    Period17/07/1621/07/16

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    Cite this

    Zhang, R., Xu, Y., Dong, Z. Y., Kong, W., & Wong, K. P. (2016). A composite k-nearest neighbor model for day-ahead load forecasting with limited temperature forecasts. In 2016 IEEE Power and Energy Society General Meeting [7741097] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/PESGM.2016.7741097