Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

Neural Network Load Forecasting Model Based on BP

Authors
Xi Gao
Corresponding Author
Xi Gao
Available Online June 2016.
DOI
https://doi.org/10.2991/mecs-17.2017.41How to use a DOI?
Keywords
neural network model, nonlinear regression, Short-term load forecasting
Abstract
Short-term load forecasting is the basis of power system operation and analysis, which is of great significance for unit commitment, economic load dispatching, safety checking and so on. As a result, improving the accuracy of load prediction is an important way to ensure the scientific decision-making of power system optimization. In this paper, based on the annual load data in two regions, we predict the discrete data by the BP neural network load forecasting model, to get the load forecast data for a given period of time in the future. In the end, the advantages and disadvantages of the two regions are evaluated, and we can get the better prediction model.
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Proceedings
2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2016
ISBN
978-94-6252-352-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecs-17.2017.41How 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  - Xi Gao
PY  - 2016/06
DA  - 2016/06
TI  - Neural Network Load Forecasting Model Based on BP
BT  - 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.41
DO  - https://doi.org/10.2991/mecs-17.2017.41
ID  - Gao2016/06
ER  -