Study On Optimization Of Energy Consumption Structure In Beijing,Tianjin,and Hebei Based On Improved Particle Swarm Optimization

As the material basis and basic guarantee of the development of national economic,energy has a great impact on the national economic construction. With the continuous development of economy, society's demand for energy is increasing, and all kinds of energy issues are followed. With the rapid development of Beijing, Tianjin, and Hebei region, and the economic center of Tianjin, which is determined as the economic center of the north, the region is becoming more and more attention in China and the whole world. Energy consumption of the region is coal-based, clean energy and renewable energy, such as natural gas, wind energy, and solar energy, began to grow in recent years. As the capital of our country, the economic of Beijing is developing rapidly since the reform and opening up to the outside world. Economic development rely on energy, while energy self-sufficiency rate is only about 50% currently. As one of the four municipalities of China, Tianjin is the largest coastal open city in northern China. While Tianjin is also a typical energy input city. The energy use efficiency of Tianjin is low. The economic growth of Hebei is rapid, but its economic development pattern is the extensive development pattern that has a strong dependence on energy consumption. With the rapid development of economy, some issues are appearing, such energy shortage and irrational energy consumption construction. At present,Hebei province is facing a serious energy crisis. These show that the coal-based energy consumption construction has led to the contradiction among economy, energy and environment of Beijing, Tianjin, and Hebei Region, and therefore, it is urgent to optimize the energy consumption structure in this area.This paper analyzed the status of energy consumption and the main energy consumption in the whole region of Beijing, Tianjin, and Hebei, energy consumption and the main energy consumption in these three regions. Then, it studied the relationship between energy consumption and economic growth in this region by means of stationary test, Granger causality test, vector auto regressive model(VAR), cointegration test, and error correction model(ECM). This paper found out the potential impact factors of energy consumption in this region, analyzed these factors in detail, calculated the correction coefficient between factors and energy consumption in this region using correlation analysis method, sorted the correction coefficient, and chose the first two factors which have higher correction coefficient as the input vector of neural network to predict the future energy consumption in this region. In addition, it used genetic algorithm to optimize weights and thresholds of neural network, to achieve better prediction result. Finally, according to the result of research and prediction, it determined optimization objectives and constraints of energy consumption structure,established multi-objective optimization model of energy consumption structure, and solved the model by particle swarm optimization(PSO). Aiming at the problem of poor local research ability of particle swarm optimization, this paper proposed synchronous particle local search(SPLS) and fuzzy global best position f-gbest, that is fuzzy multi-objective particle swarm optimization(FMOPSO), to solve the optimization model. According to optimization result, this paper put forward countermeasures and suggestions for the future energy development in Beijing, Tianjin, and Hebei Region.