Power Load Forecasting Based on Neural Network and Time Series

In the analysis of predicting power load forecasting based on least squares neural network, the instability of the time series could lead to decrease of prediction accuracy. On the other hand,neural network and chaos theories parameters must be carefully predetermined in establishing an efficient model. In order to solve the problems mentioned above, in this paper, the neural network and chaos theory was established. It can be seen that possessed chaotic features, providing a basis for performing short-term forecast of power load with the help of neural network theory. Chaotic Time Series method is used to find the optimal time lag. Then the time series is decomposed by wavelet transform to eliminate the instability. Chaotic Time Series method is adopted to determine the parameters of neural network. Additionally, the proposed model was tested on the prediction of share price of one listed company in China. Especially, In order to validate the rationality of chosen dimension, the other dimensions were selected to compare with the calculated dimension. And to prove the effectiveness of the model, neural network algorithm was used to compare with the result of chaos theory. Experimental results showed that the proposed model performed the best predictive accuracy and generalization, implying that integrating the wavelet transform with neural network model can serve as a promising alternative for power load forecasting.