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

Electricity Consumption Forecast of Hunan Province Using Combined Model Based on Multivariate Linear Regression and BP Neural Network

Authors
Yan Li, Shuyu Dai, Dongxiao Niu
Corresponding Author
Yan Li
Available Online December 2017.
DOI
https://doi.org/10.2991/mcei-17.2017.138How to use a DOI?
Keywords
Multivariate linear regression; BP neural network; Combined model; Electricity consumption forecast of hunan province
Abstract
In recent years, the low energy consumption level and shortage of energy resources have become key factors restricting energy development and structural adjustment of Hunan Province. Therefore, load forecasting does play a significant role in making reasonable grid plan for Hunan Province. In this paper, by collecting and analyzing data of Hunan Power Grid, we identified the factors affecting electricity consumption, then used multiple linear regression method and BP neural network to predict the electricity consumption in the next nine years. The results showed that, the growth rate of electricity consumption of Hunan Province in next nine years is lower than that of the past ten years.
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This is an open access article distributed under the CC BY-NC license.

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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-17.2017.138How 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  - Yan Li
AU  - Shuyu Dai
AU  - Dongxiao Niu
PY  - 2017/12
DA  - 2017/12
TI  - Electricity Consumption Forecast of Hunan Province Using Combined Model Based on Multivariate Linear Regression and BP Neural Network
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-17.2017.138
DO  - https://doi.org/10.2991/mcei-17.2017.138
ID  - Li2017/12
ER  -