Abstract
China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.
Highlights
The main contents of the article are as follows: the second section describes the mathematical principle of robust principal component analysis and particle swarm optimization (PSO)-Least Squares Support Vector Machine (LSSVM) optimized by the Tabu search algorithm; the third part proves that the proposed forecasting model has higher prediction accuracy, generalization ability, and higher training speed by compared results with traditional LSSVM and PSO-LSSVM models, and we apply the model to forecast the energy consumption in China from 2017 to 2030; the fourth part makes forward-looking conclusions according to the results of the RPCA-TS-PSO-LSSVM forecasting model
From the perspective of mathematical statistics, we find that TS-PSO-LSSVM< PSO-LSSVM
From the perspective of root means square error (RMSE), which indicates the TS-PSO-LSSVM model in this article has the best performance from the perspective of degree of dispersion
Summary
The traditional methods to forecast energy consumption mainly includes mathematical statistics methods, such as linear regression, time series analysis, gray prediction, etc These methods all regard energy consumption as a linear problem, which is greatly limited by the choice of influencing factors, so it is difficult to be rational and scientific. The main contents of the article are as follows: the second section describes the mathematical principle of robust principal component analysis and PSO-LSSVM optimized by the Tabu search algorithm; the third part proves that the proposed forecasting model has higher prediction accuracy, generalization ability, and higher training speed by compared results with traditional LSSVM and PSO-LSSVM models, and we apply the model to forecast the energy consumption in China from 2017 to 2030; the fourth part makes forward-looking conclusions according to the results of the RPCA-TS-PSO-LSSVM forecasting model
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