Affiliations: [a] College of Engineering, Honghe University, Mengzi, Yunnan, China
| [b]
Center of Comprehensive Testing, Quality and Technical Supervision of Honghe Prefecture, Mengzi, Yunnan, China
| [c] Power Plant, YEIG Malong New Energy Generation CO., LTD, Yunnan Energy Investment, Malong, Yunnan, China
Correspondence:
[*]
Corresponding author. Zhimei Duan, College of Engineering, Honghe University, Mengzi 661199, Yunnan, China.
E-mail: duanzhimei2002@163.com.
Abstract: Energy is an indispensable material resource for human production and life. It is a powerful engine and an important guarantee for human survival, economic and social sustainable development and world change. The economy is developing rapidly, the demand for energy continues to grow, energy consumption has increased sharply in a short period, and the security of energy supply and demand has also shown a severe trend. Predicting energy demand is especially important. However, due to the many influencing factors and the lack of energy data, the energy demand prediction has great uncertainty in the prediction results. Because of the above problems, this paper proposes an energy big data demand prediction model based on a fuzzy rough set model. Firstly, according to the data, the factors affecting the energy demand are determined, and the fuzzy C-means clustering algorithm is used to discretize the data according to the characteristics of the fuzzy rough set. Then the decision table is established and the attribute importance is calculated, and then the neighborhood rough set is used for attribute reduction. Then extract the correlation rules to establish a prediction model. Compare the prediction model proposed in this paper with the existing gray prediction method and energy elasticity coefficient method. The results show that this method can more scientifically predict the changes in energy big data demand. Finally, based on the experimental results, the corresponding strategies for optimizing the energy structure are proposed to provide reference for the optimization and development of energy demand.
Keywords: Energy big data, fuzzy rough set, demand prediction, structure optimization
