Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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An Approach of Dimension Reduction in k-Nearest Neighbor Based Short-term Load Forecasting

  • Chu, FaZheng (Economics and Management College of Qingdao Agricultural University) ;
  • Jung, Sung-Hwan (Dept. of Computer Engineering, Changwon National University)
  • Received : 2017.07.04
  • Accepted : 2017.08.31
  • Published : 2017.09.30
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Abstract

The k-nearest neighbor (k-NN) algorithm is one of the most widely used benchmark algorithm in classification. Nowadays it has been further applied to predict time series. However, one of the main concerns of the algorithm applied on short-term electricity load forecasting is high computational burden. In the paper, we propose an approach of dimension reduction that follows the principles of highlighting the temperature effect on electricity load data series. The results show the proposed approach is able to reduce the dimension of the data around 30%. Moreover, with temperature effect highlighting, the approach will contribute to finding similar days accurately, and then raise forecasting accuracy slightly.

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References

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