Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)

Research on Power Demand Forecasting of Beijing Based on Regression Analysis

Authors
Shuyu Dai, Yan Li, Dongxiao Niu
Corresponding Author
Shuyu Dai
Available Online December 2017.
DOI
https://doi.org/10.2991/mcei-17.2017.56How to use a DOI?
Keywords
Electricity demand forecast; Unitary regression; Multiple regression
Abstract
With the continuous development of the national economy, people's demand for electricity is increasing day by day, and the power industry plays an increasingly important role in the social development. Power demand forecasting is the basic work of power management, planning, programming and other management departments in power system. Improving the technical level of power demand forecasting can provide a strong basis for power system scheduling and long-term planning. This paper analyzes the influencing factors of electricity demand in Beijing, analyzes the influence of GDP, industrial structure, population and urbanization level on the electricity consumption of Beijing, and then uses the regression analysis method based on the collected economic data and power data to forecast the electricity consumption of the whole society in Beijing from 2017 to 2022.
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Proceedings
2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
Part of series
Advances in Computer Science Research
Publication Date
December 2017
ISBN
978-94-6252-430-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/mcei-17.2017.56How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Shuyu Dai
AU  - Yan Li
AU  - Dongxiao Niu
PY  - 2017/12
DA  - 2017/12
TI  - Research on Power Demand Forecasting of Beijing Based on Regression Analysis
BT  - 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/mcei-17.2017.56
DO  - https://doi.org/10.2991/mcei-17.2017.56
ID  - Dai2017/12
ER  -