• Energy Research

In early 2013, Beijing and even the whole country of china appeared the haze weather and PM2.5 problem, environmental problems caused widespread attention again. The latest research of "Atmospheric haze tracking and control" special group of CAS results that: coal and motor vehicles are the main cause of the strong fog haze weather. For China, the coal is the primary energy in China accounted for the largest. Along with the energy-saving emission reduction as a major national policy background, the analysis of coal consumption market has increasingly aroused the attention of scientific researchers, and become the hotspot of relative research recent years. In order to solve the problem, this paper takes Shaanxi Province as an object of study, which is one of three major coal producing regions. On the basis of analyzing the core factors affecting extraction coal demand, establish VAR model and BVAR model of coal consumption. Finally, by comparing the three kinds of results of two model prediction, using BVAR model predict the coal consumption in Shaanxi in 2020 will reach 175792800 tons of standard coal.

Abstract As one of the four major industrial raw materials in the world, natural rubber is closely related to the national economy and people’s livelihood. The analysis of natural rubber price and volatility can give hedging guidance to manufacturers and provide investors with uncertainty and risk information to reduce investment losses. To effectively analyses and forecast the natural rubber’s price and volatility, this paper constructed a hybrid model that integrated the bidirectional gated recurrent unit and variational mode decomposition for short-term prediction of the natural rubber futures on the Shanghai Futures Exchange. In data preprocessing period, time series is decomposed by variational mode decomposition to capture the tendency and mutability information. The bidirectional gated recurrent unit is introduced to return the one-day-ahead prediction of the closing price and 7-day volatility for the natural rubber futures. The experimental results demonstrated that: (a) variational mode decomposition is an effective method for time series analysis, which can capture the information closely related to the market fluctuations; (b) the bidirectional neural network structure can significantly improve the model performance both in terms of fitting performance and the trend prediction; (c) a correspondence was found between the predicted target, i.e., the price and volatility, and the intrinsic modes, which manifested as the impact of the long-term and short-term characteristics on the targets at different time-scales. With a change in the time scale of forecasting targets, it was found that there was some variation in matching degree between the forecasting target and the mode sub-sequences.

A number of recent studies have adopted long short-term memory (LSTM) in extensive applications, such as handwriting recognition and time series prediction, with considerable success. However, the parameters of LSTM have greatly influenced its accuracy and performance. In this study, LSTM with fruit fly optimization algorithm (FOA), called FOA-LSTM, is designed to solve time series problems. As a novel intelligent algorithm, FOA is applied to decide on the optimal hyper-parameter of LSTM. Experiments under the NN3 time series, three comparative experiments and the monthly energy consumption of the USA are conducted to verify the effectiveness of the FOA-LSTM model. The results indicate that the symmetric mean absolute percentage error (SMAPE) is reduced by up to 11.44% in the last 11 monthly series in the NN3 dataset. Four comparative experiments and the real-life series verify further that the FOA-LSTM model obtains a better result compared with other forecasting models.

Considering the speedy growth of industrialization and urbanization in China and the continued rise of coal consumption, this paper identifies factors that have impacted coal consumption in 1985–2011. After extracting the core factors, the Bayesian vector autoregressive forecast model is constructed, with variables that include coal consumption, the gross value of industrial output, and the downstream industry output (cement, crude steel, and thermal power). The impulse response function and variance decomposition are applied to portray the dynamic correlations between coal consumption and economic variables. Then for analyzing structural changes of coal consumption, the exponential smoothing model is also established, based on division of seven sectors. The results show that the structure of coal consumption underwent significant changes during the past 30 years. Consumption of both household sector and transport, storage, and post sectors continues to decline; consumption of wholesale and retail trade and hotels and catering services sectors presents a fluctuating and improving trend; and consumption of industry sector is still high. The gross value of industrial output and the downstream industry output have been promoting coal consumption growth for a long time. In 2015 and 2020, total coal demand is expected to reach 2746.27 and 4041.68 million tons of standard coal in China.