Aviation fuel demand development in China

Chai, Jian; Zhang, Zhongyu; Wang, Shouyang; Lai, Kin Keung; Liu, John

doi:10.1016/j.eneco.2014.09.007

Cited by 12 publications

(1 citation statement)

References 33 publications

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“…It should be noted that although the ANNs model can describe the nonlinear characteristics of electricity price series, it cannot well deal with the linear fitting problem [27]. To describe the linear features of electricity prices, the time series model is often applied, which is considered as one of the most effective techniques [28]. Traditional time series models, such as autoregressive integrated moving average (ARIMA), autoregressive and moving average (ARMA) and generalized autoregressive conditional heteroscedasticity (GARCH), have been frequently applied to forecast electricity prices.…”

Section: Introductionmentioning

confidence: 99%

An accurate analysis of the parameters affecting consumption and price fluctuations of Electricity in the Iranian market during Summer

et al. 2020

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In this paper, a novel method is proposed to predict the cost of short-term hourly electrical energy based on combined neural networks. In this method, the influential parameters that play a key role in the accuracy of these systems are identified and the most prominent ones are selected. In the proposed method, initially, using the SOM network, similar days are placed in close clusters. In the next stage, the temperature parameter and prices pertaining to similar days are trained separately in two MLP neural networks because of their differences concerning the range of changes and their nature. Finally, the two networks are merged with another MLP network. In the proposed hybrid method, an evolutionary search method is used to provide an appropriate initial weight for neural network training. Given the price data changes, the price amidst the previous hour has a significant effect on the prediction of the current state. In this vein, in the proposed method, the predicted data in the previous hour is considered as one of the inputs of the next stage. The proposed method was assessed on the datasets of Iran in the summer. This information pertains to the 2011-2016 period.

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Section: Introductionmentioning

confidence: 99%