『Abstract
Electricity demand forecasting could prove to be a useful policy
tool for decision-makers; thus, accurate forecasting of electricity
demand is valuable in allowing both power generators and consumers
to make their plans. Although a seasonal ARIMA model is widely
used in electricity demand analysis and is a high-precision approach
for seasonal data forecasting, errors are unavoidable in the forecasting
process. Consequently, a significant research goal is to further
improve forecasting precision. To help people in the electricity
sectors make more sensible decisions, this study proposes residual
modification models to improve the precision of seasonal ARIMA
for electricity demand forecasting. In this study, PSO optimal
Fourier method, seasonal ARIMA model and combined models of PSO
optimal Fourier method with seasonal ARIMA are applied in the
Northwest electricity grid of China to correct the forecasting
results of seasonal ARIMA. The modification models forecasting
of the electricity demand appears to be more workable than that
of the single seasonal ARIMA. The results indicate that the prediction
accuracy of the three residual modification models is higher than
the single seasonal ARIMA model and that the combined model is
the most satisfactory of the three models.
Keywords: Electricity demand; Seasonal ARIMA; Residual modification
model』
1. Introduction
2. Current energy status and policy in China
3. Review of the seasonal ARIMA model
4. PSO optimized Fourier residual modification approach
5. Residual modification of S-ARIMA
6. Combined Fourier and S-ARIMA residual modification model
7. Analysis results
8. Conclusion
Acknowledgments
References