daily coal consumption of power plantsARIMA modelshort-term prediction煤炭日耗数据是反映工业活动的重要指标,具有较强的跟踪意义,研究短期内沿海电厂煤炭日耗变化有助于预判煤炭市场走势及需求变化情况.运用时间序列分析法对沿海电厂煤炭日耗数据进行建模,得到适用的ARIMA模型后,对后续日耗数据进行短期
As an important link in the complex system engineering project of open pit mining, the quality of the boundary determines the performance of the project to a large extent. However, changes in economic indicators may raise doubts about the optimality of m
(2013) utilized the ARIMA model to predict the price of carbon effectively. Paolella and Taschini (2008) applied the GARCH model to predict the carbon price and found this model had certain accuracy. Byun and Cho (2013) adopted the GARCH family model to predict carbon futures volatility ...
auto-regressive integrated moving average (ARIMA) and autoregressive (AR) and support vector regression (SVR) (Oliveira and Ludermir, 2016) is also suggested for time series forecasting where PSO is used to optimize the order of AR model, SVR parameters and number of lags of the time series....
2, Tapio identified eight types of decoupling states based on the calculated decoupling elasticity values, where \(\text{e}(\text{C},\text{G}\text{D}\text{P})\) is the decoupling elasticity value between industrial land carbon emissions and the total industrial output value in the East ...
consumption, whereas 65–70% of the samples were deemed suitable. The insights from the PIG and WPI model also revealed that lead (Pb) was the most influential PTE that degraded the quality of groundwater resources in the research area. The findings of the MLR and ANN models indicated ...
To deal simultaneously with the environmental problems caused by the current high-intensity exploitation and extensive use of coal resources, it is necessary to perform a scientific prediction of the trend and, especially, the peak of China’s coal demand. Based on the historical data on coal con...
As the main objective of this research is to develop an energy consumption predictive model for smart commercial building by using several machine learning methods in a cloud-based machine learning platform, this research focuses more on the accuracy of the methodology applied in predicting energy con...
Gray economic model Oil price Oil crisis Peakoil 1. Introduction Different Crude oil prices are among the most important key variables that have a significant impact on the performance strategy of international financial markets [1]. Therefore, forecasting oil prices not only plays an effective role...
In the literature [37], a classification modeling method of day-ahead price prediction based on daily pattern prediction is proposed. The basic idea is to extract the price models of the predicted period from the conventional day-ahead prediction results, and then build the prediction models for...