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Power Load Prediction Based on Fractal Theory

Author

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  • Liang Jian-Kai
  • Carlo Cattani
  • Song Wan-Qing

Abstract

The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and load curve drawing. The attractor is obtained using an improved deterministic algorithm based on the fractal interpolation function, a day’s load is predicted by three days’ historical loads, the maximum relative error is within 3.7%, and the average relative error is within 1.6%. The experimental result shows the accuracy of this prediction method, which has a certain application reference value in the field of short-term load prediction.

Suggested Citation

  • Liang Jian-Kai & Carlo Cattani & Song Wan-Qing, 2015. "Power Load Prediction Based on Fractal Theory," Advances in Mathematical Physics, Hindawi, vol. 2015, pages 1-6, March.
    • Handle: RePEc:hin:jnlamp:827238
    DOI: 10.1155/2015/827238
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    Cited by:

    1. Song, Wanqing & Cattani, Carlo & Chi, Chi-Hung, 2020. "Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach," Energy, Elsevier, vol. 194(C).

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