Volume 7, Issue 3, June 2018, Page: 40-46
Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model
Mengyao Mei, Department of Electrical Engineering, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China
Lili Ma, Department of Economic Management, Rongcheng Campus of Harbin University of Science and Technology, Rongcheng, China
Zhihong Liu, Software Engineering Department, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China
Zhongxian Zhu, Software Engineering Department, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China
Jianan Li, Department of Electrical Engineering, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China
Xiaohan Fang, Software Engineering Department, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China
Received: Nov. 7, 2018;       Published: Nov. 8, 2018
DOI: 10.11648/j.ijepe.20180703.13      View  90      Downloads  6
Abstract
With the continuous increase of China's total energy consumption, we can find the rule and grasp its development trend from the change trend of energy consumption. In order to provide scientific basis for rational use of energy. In this paper,firstly, based on the data of total energy consumption of shandong province from 2007 to 2016, grey prediction model and BP neural network were used to predict total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted value of each year and the average relative error of the two models was 7.25% and 3.70% respectively. Secondly, on the basis of the grey prediction model, BP neural network was used to correct the predicted value of total energy in shandong province. Then, the grey BP modified model was used to obtain the total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted values of each year and the average relative error of the modified model was 2.04%. Finally, the total energy consumption of shandong province in 2018-2035 is predicted. The results show that the average relative error is small and the prediction effect is obvious. This shows that the grey BP model is effective in predicting total energy consumption.
Keywords
Grey Prediction, BP Neural Network, Grey BP Model, Total Energy Consumption
To cite this article
Mengyao Mei, Lili Ma, Zhihong Liu, Zhongxian Zhu, Jianan Li, Xiaohan Fang, Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model, International Journal of Energy and Power Engineering. Vol. 7, No. 3, 2018, pp. 40-46. doi: 10.11648/j.ijepe.20180703.13
Reference
[1]
Zhang Ming, Mu Hailin. Study on passenger transportation energy consumption in China's megacities based on LMDI decomposition method [J]. ActaScientiarum Naturalium Universitatis Pekinensis, 2010, 46 (03):483-486.
[2]
Hang Chenzhe, XuDinghua, Ma Guoyuan, Zhang Haiyun, TengJunheng. Study on uncertainty evaluation method of annual energy consumption efficiency measured by air enthalpy method [J]. ActaMetrologica Sinica, 2017, 38 (01):34-39.
[3]
Dai Xiaowen, He Yanqiu, ZhongQiubo. Research on driving factors and contribution of agricultural energy consumption carbon emissions in China—Based on Kaya identity expansion and LMDI index decomposition method [J]. Chinese Journal of Eco-Agriculture, 2015, 23 (11):1445-1454.
[4]
Hu Bentian, Fang Chao. An empirical study on regional economic growth and energy consumption in Wanjiang City Belt——Based on Tapio decoupling model and LMDI method [J]. Journal of Tongling University, 2014, 13 (02):68-73.
[5]
Wang Yongli, Shi Dan, Han Jiqiong. Research on energy consumption in China based on multiple regression trial analysis model [J]. Management & Technology of SME, 2014 (11):145-146.
[6]
Liu Yuhai, Wu Peng. Energy consumption, carbon dioxide emissions and APEC regional economic growth—Empirical study based on SBM-Undesirable and Meta-frontier model [J]. Economic Review, 2011 (06):109-120+129.
[7]
Zeng Bo, Meng Wei, Liu Sifeng, Li Chuan, Cui Jie. Grey heterogeneous data prediction modeling method for disaster emergency material demand [J]. Chinese Journal of XuXingjun, Yan Gangfeng. Analysis of stock price trend based on BP neural network [J]. Zhejiang finance, 2011 (11):57-59+64. Management Science, 2015, 23 (08): 84-91.
[8]
Wang Liping, Li Shuqin. The impact of FDI on China's low-carbon economy is based on China's data test from 1992 to 2016 [J]. Resource development and market, 1988, 34 (10):1438-1443.
[9]
Zhang Jing. Prediction of PM2.5 concentration in shenyang city based on BP neural network [A]. China meteorological society. Annual meeting of the 35th China meteorological society, S12 atmospheric composition and weather, climate change and environmental impact and environmental weather forecast and impact assessment [C].
[10]
Guo Xue. Grey correlation analysis of air quality influencing factors in taiyuan city [A]. China meteorological society. Annual meeting of the 35th China meteorological society, S12 atmospheric composition and weather, climate change and environmental impact and environmental weather forecast and impact assessment [C]. China meteorological society: China meteorological society, 2018:5.
[11]
Decision of the standing committee of the people's congress of sichuan province to amend the "implementation measures of > of energy conservation law of the People's Republic of China" [N]. Sichuan daily, 2018-10-10 (010).
[12]
Zhao Wenqiang. Discussion on energy alternatives based on a low carbon background [J]. Low carbon world, 2018 (10):152-154.
[13]
Tian Yingnan. Study on optimal design of distributed cold and thermal power energy system and comprehensive evaluation method of multiple indexes [J]. Economic and trade practice, 2018 (19):275.
[14]
Ceng Qing. The price of carbon emission rights in China, the influence of two kinds of energy shares - comparative analysis based on VECM model [J/OL]. Financial development research: 1-9 [2018-10-11]. https://doi.org/10.19647/j.cnki.37-1462/f.2018.10.009.
[15]
Li Yiqing, Liu Qianjin, Zhou Baorong, Liu Shiping, Cheng Lanfen. Application of power generation rights trading that takes into account carbon trading benefits in clean energy [J]. Value engineering, 2008, 37 (31):186-189.
Browse journals by subject