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

Guo-Feng Fan,Wei-Chiang Hong,An Wang

doi:10.3390/en11071625

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.

Highlights

Based on these obtained parameters, and the actual natural gas consumption values of China in 2002–2010, the simulation values of the GM(1,1) and the self-adapting intelligent grey model (SIGM) are put into the GM-SIGM-genetic algorithm (GA) model to calculate the combined weight coefficients, w1 and w2
This paper proposes a novel combined grey-based annual natural gas consumption forecasting model, by combining the GM(1,1) and the SIGM with genetic algorithm and the annual share changing tendency mechanism of the natural gas consumption from the total energy consumption, namely GM-S-SIGM-GA model
The experimental results indicate that the proposed GM-S-SIGM-GA model significantly outperforms to other grey-based forecasting models

Summary

Introduction

As shown in the 13th Five-Year Plan of China, the development of natural gas industry has been an important national policy, in addition, due to environmental management and rapid increase in natural gas consumption that cause the imbalance. The electricity utility companies are motived to receive accurate natural gas consumption for their integrated demand response program switching the energy resources (from the electricity to the natural gas) during the peak hours [3,4]. Electricity utility companies should collaborate together to achieve sustainability in terms of providing more realistic forecasting models of interdependent natural gas demand networks [5]. It is very important to accurately forecast the demand of natural gas in China for the government and energy companies to formulate energy policies

Methods

Results

Conclusion