Neural Network Load Forecasting Model Based on BP
Available Online June 2016.
- https://doi.org/10.2991/mecs-17.2017.41How to use a DOI?
- neural network model, nonlinear regression, Short-term load forecasting
- 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.
- 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 - 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 - https://doi.org/10.2991/mecs-17.2017.41 ID - Gao2016/06 ER -