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ID:
115128
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| Publication |
2012.
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| Summary/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.
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| 2 |
ID:
179751
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| Summary/Abstract |
This paper studies the moderating effect of local government intervention on transforming feed-in tariffs and knowledge stocks into renewable energy technology innovation. We examine the key intervention measures for the wind energy sector, using provincial panel data in China over the period 2008–2017. Our results show that local government intervention factors, such as policy count in renewables and R&D expenditure, are significant drivers for technology innovation. However, local green fixed industrial investment has negative implication. Moreover, we find that increasing local policy counts in renewables and augmenting local R&D investment can buffer feed-in tariffs’ effectiveness on innovation performance. Expanding renewable policies, and increasing local fund on R&D program and green fixed industries may enhance the relationship between knowledge stocks and technology innovation. It is suggested that the provinces with over-reliance subsidies should put more emphasis on local renewable development policies and increase the level of R&D expenditure.
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