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Abstract

The power load influenced by many random factors shows the nonlinear chaotic characteristics. At the present, the research on the load forecasting by using the chaos theory mainly focuses on the short term local load forecasting and the certification of chaotic characteristics is insufficient. In this study, the chaotic characteristics of Chinese medium term power load will be made a full proof and the chaotic neural network improved by the theory of Euclidean distance will be used to make a precise forecasting. Firstly, the data of the total electricity consumption from 70 months were processed by the first order difference after taking logarithm. Secondly, the Wolf algorithm and G_P algorithm were adopted to obtain maximal Lyapunov exponent and the fractal dimension, which proved that there are chaotic characteristics of Chinese medium term power load. Finally, the chaotic neural network based on the Euclidean distance was used to forecast. The results show an excellent effect of the method. There is a premising application of the chaotic neural network based on the Euclidean distance in the study of load forecasting.

Keywords

Chaos Euclidean distance neural network load forecasting

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Medium term load forecasting for chaotic neural networks based on the Euclidean distance

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