An improved grey model WD-TBGM (1, 1) for predicting energy consumption in short-term
- PDF / 2,251,971 Bytes
- 23 Pages / 439.37 x 666.142 pts Page_size
- 75 Downloads / 220 Views
An improved grey model WD‑TBGM (1, 1) for predicting energy consumption in short‑term Jie Li1 · Yelin Wang1 · Bin Li2 Received: 12 March 2020 / Accepted: 3 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The traditional grey model has been widely used for predicting energy consumption (EC) in short-term with a small sample-size, but its accuracy is greatly affected by data fluctuation. In order to further improve the prediction performance while considering the data fluctuation, in this study, the wavelet de-noising is introduced to pre-processing the EC data as the input of a modified grey model, leading to an improved novel grey model WD-TBGM (1, 1). It is found that using a wavelet decomposition algorithm can denoise the data and then the data fluctuation is effectively reduced. After illustrating effectiveness by numerical simulation and case study, the prediction performance of this newly proposed hybrid model can be enhanced with approximately 5% compared with the classical grey models. Furthermore, this newly proposed hybrid model is used to address the issues of EC prediction in China which is one of the worldwide top ten energy consumers and in Shanghai city which is one of the top energy consumers in China. The forecasting results show that the total EC of China and Shanghai will slow down in the next few years, which is in line with their actual development situation. This research also explains the effectiveness of the energy conservation and emission reduction policies that China and Shanghai are taking. Keywords Improved GM (1, 1) · Wavelet de-noising · Energy consumption prediction · Time series model · The combination model
* Yelin Wang [email protected] Jie Li [email protected] Bin Li [email protected] 1
Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650093, People’s Republic of China
2
Robert C. Vackar College of Business and Entrepreneurship, University of Texas Rio Grande Valley, 1201 W University Dr, Edinburg, TX 78539, USA
13
Vol.:(0123456789)
J. Li et al.
1 Introduction For a region, the era of open development has passed, and the energy-saving is an inevitable trend of development [1, 2]. In a region, energy consumption is usually an important index of building energy-saving structures, the prediction of it is an important reference for the construction of a macroeconomic plan, and its accuracy is directly related to the rationality and effectiveness of the policy [3, 4]. Just like the reliable prediction of EC of China in the 13th 5-year plan (2016–2020), it is of great significance to ensure the sustainable development of energy and economy in China [5]. Energy consumption is not only used as a reference for formulating specific policies, but also used to explore the relationship between EC and other factors, in order to make suggestions on the general direction of development. Ozcan and Ozturk [6] discussed the relationship between renewable energy consumption and growth of economic, and fo
Data Loading...
