Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting

Abstract

Along with the high growth rate of economy and fast increasing air pollution, clean energy, such as the natural gas, has played an important role in preventing the environment from discharge of greenhouse gases and harmful substances in China. It is very important to accurately forecast the demand of natural gas in China is for the government to formulate energy policies. This paper firstly proposes a combined forecasting model, name GM-S-SIGM-GA model, to forecast the demand of natural gas in China from 2011 to 2017, by constructing the grey model (GM(1,1)) and the self-adapting intelligent grey model (SIGM), respectively; then, it employs a genetic algorithm to determine the combined weight coefficients between these two models. Finally, using the tendency index (the annual changes of the share of natural gas consumption from the total energy consumption), which completely reveal the annual natural gas consumption share among the market, to successfully adjust the fluctuated changes for each data period. The natural gas demand data from 2002 to 2010 in China are used to model the proposed GM-S-SIGM-GA model, and the data from 2011 to 2017 are used to evaluate the forecasting accuracy. The experimental results demonstrate that the proposed GM-S-SIGM-GA model is superior to other single forecasting models in terms of the mean absolute percentage error (MAPE; 4.48%), the root mean square error (RMSE; 11.59), and the mean absolute error (MAE; 8.41), respectively, and the forecasting performances also receive the statistical significance under 97.5% and 95% confident levels, respectively.

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

“…Others Decomposition approach [83,84] Input-output model [85,86] Bottom-up model [87][88][89] Grey method [26,37,76,86,90,91] Logistic model [92] As shown in Table 1 and noted before learning based techniques can lead to develop a more reliable, accurate estimator as they can represent a self-adjustment characteristic since they learn formerly signals via feedback loops. To dedicate a more detailed understanding of various existed forecasting models, Table 2 shows the pros and cons of main forecasting methods.…”

Forecasting China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method

“…One is based on uncertain information for prediction. They use grey theory [17,18], Bayesian average model [19], logistic model [20], etc. The other is predicted by a combined intelligent algorithm.…”

“…The other is predicted by a combined intelligent algorithm. That is, genetic algorithm [17,21], neural network algorithm [5,11,22,23], support vector machine [24][25][26][27][28], particle swarm algorithm [11,17,25], simulated annealing algorithm [21] and other combinations. In the research results of natural gas consumption forecasting, considering the prediction of uncertainties in natural gas consumption, the concepts of grey theory [17,18], Bayesian average model [19], logistic model [20], etc.…”

“…That is, genetic algorithm [17,21], neural network algorithm [5,11,22,23], support vector machine [24][25][26][27][28], particle swarm algorithm [11,17,25], simulated annealing algorithm [21] and other combinations. In the research results of natural gas consumption forecasting, considering the prediction of uncertainties in natural gas consumption, the concepts of grey theory [17,18], Bayesian average model [19], logistic model [20], etc. are adopted; Intelligent algorithm effectively improves prediction accuracy, namely genetic algorithm [17,21], neural network algorithm [11,22,23,29], support vector machine [24][25][26][27][28], particle swarm optimization [11,17,25], simulated annealing algorithm [21] and other combinations.…”

“…In the research results of natural gas consumption forecasting, considering the prediction of uncertainties in natural gas consumption, the concepts of grey theory [17,18], Bayesian average model [19], logistic model [20], etc. are adopted; Intelligent algorithm effectively improves prediction accuracy, namely genetic algorithm [17,21], neural network algorithm [11,22,23,29], support vector machine [24][25][26][27][28], particle swarm optimization [11,17,25], simulated annealing algorithm [21] and other combinations. Jolanta Szoplik et al [22] used the neural network method and Bai et al [24] used Support Vector Machines, constructed the prediction model of natural gas consumption, and calculated the forecast of daily natural gas consumption.…”