E3S Web Conf.
Volume 233, 20212020 2nd International Academic Exchange Conference on Science and Technology Innovation (IAECST 2020)
|Number of page(s)||4|
|Section||NESEE2020-New Energy Science and Environmental Engineering|
|Published online||27 January 2021|
Enterprise Power Consumption Data and GDP Forecasting Based on Ensemble Algorithms
1 Marketing Service Center of State Grid Gansu Electric Power Company, Gansu, 730000, China
2 Jiayuguan Power Supply Company of State Grid Gansu Province Electric Power Company, Gansu, 730000, China
3 Tianshui Power Supply Company of State Grid Gansu Electric Power Company, Gansu, 730000, China
4 Dingxi Power Supply Company of State Grid Gansu Electric Power Company, Gansu, 730000, China
5 Jinchang power supply Company of State Grid Gansu Electric Power Company, Gansu, 730000, China
a Corresponding author: firstname.lastname@example.org
This paper focuses on the development of regional GDP and proposes a method proposed for forecast of enterprise power consumption data and GDP based on ensemble algorithms. The enterprise power consumption data are used as independent variables and GDP data as dependent variables. A multiple linear regression model is selected as the primary learner for training and its outputs will be sorted into a new dataset of input features to train a secondary learner. The forecast of GDP is thus realized through ensemble learning.
© The Authors, published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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