Proceedings of the 2016 International Conference on Education, Management Science and Economics

Prediction of Electricity Consumption Based on Multiple Liner Regression

Authors
He-rui Cui, Ting-ting Wu
Corresponding Author
He-rui Cui
Available Online December 2016.
DOI
https://doi.org/10.2991/icemse-16.2016.50How to use a DOI?
Keywords
Multiple liner regression, Hebei network, electricity consumption forecast, power regional economy
Abstract
The prediction of power load is important content, prerequisite and basis of the planning system and running of power grid. The healthy development of power industry is based on scientific and accurate prediction. Multivariate linear regression analysis is a method to analyze the linear relationship between one dependent variable and multiple independent variables. The accuracy of load forecasting directly affects the operation of the power system, this paper studies the multiple linear regression model and its application in the regional economy of electricity. In this paper a prediction for power load was presented on the basis of multiple liner regression model. The model in this paper applied the variant GDP and population to predict the total electricity consumption of Hebei from 2000-2014. The result showed that the model is effective to the prediction of power load and further can provide foundation for the controlling and prediction of power load.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Education, Management Science and Economics
Part of series
Advances in Social Science, Education and Humanities Research
Publication Date
December 2016
ISBN
978-94-6252-275-6
ISSN
2352-5398
DOI
https://doi.org/10.2991/icemse-16.2016.50How 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  - He-rui Cui
AU  - Ting-ting Wu
PY  - 2016/12
DA  - 2016/12
TI  - Prediction of Electricity Consumption Based on Multiple Liner Regression
BT  - 2016 International Conference on Education, Management Science and Economics
PB  - Atlantis Press
SP  - 207
EP  - 210
SN  - 2352-5398
UR  - https://doi.org/10.2991/icemse-16.2016.50
DO  - https://doi.org/10.2991/icemse-16.2016.50
ID  - Cui2016/12
ER  -