Neural Network Load Forecasting Model Based on BP

DOI

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xi Gao
PY  - 2016/06
DA  - 2016/06
TI  - Neural Network Load Forecasting Model Based on BP
BT  - Proceedings of the 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  - 10.2991/mecs-17.2017.41
ID  - Gao2016/06
ER  -