Daily Load Forecasting Based on Combination Forecasting Techniques
To improve the accuracy of load forecasting is the focus of the load forecasting. As the daily load by various environmental factors and periodical, this makes the load time series of changes occurring during non-stationary random process. The key of improving the accuracy of artificial neural network training is to select effective training sample. This paper based on the time series forecasting techniques’ random time series autocorrelation function to select the neural network training samples. The method of modeling is more objective. By example, the comparison with autoregressive (AR) Model predictions and BP Artificial Neural Network (ANN) predicted results through error analysis and confirmed the proposed scheme good performance.
Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang
C. Gao et al., "Daily Load Forecasting Based on Combination Forecasting Techniques", Advanced Materials Research, Vols. 201-203, pp. 2685-2689, 2011