A New Short-Term Load Forecasting in Power Systems

Abstract:

For realizing highly accuracy load forecasting, a new method is proposed. Power load time series belongs to chaotic series. Firstly, for obtaining three parameters in chaotic theory, namely time delay, embedding dimension and the number of the nearest neighbors, self-correlation function method and G-P algorithm are used to reconstruct the phase space of chaotic time series. Secondly, ant colony optimization approach is introduced to more accurately acquire forecasting reference points, considering distance and relativity of phase points evolution in this paper. Finally, GM (1, 1) Model is applied to forecast daily load data. The actual forecasting results prove that the new approach has better forecasting accuracy and convergence.

• Periodical: Applied Mechanics and Materials (Volumes 494-495)
• Pages: 1631-1635
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.494-495.1631
• Online since: February 2014
• Authors: Hui Li*, Hong Bin Sun
• Keywords: Ant Colony Algorithm, Chaotic Theory, Short-Term Load Forecasting

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