Teaching Mode on Short-term Load Forecasting based on BP Neural Network Model

Jianjun Xu; Junhui Shao; Tingting Wang

doi:10.54691/fsd.v2i6.953

Authors

Jianjun Xu
Junhui Shao
Tingting Wang

DOI:

https://doi.org/10.54691/fsd.v2i6.953

Keywords:

Teaching Mode, BP Neural Network, Short-term Load Prediction

Abstract

Accurate prediction of the load can make the amount of power generation change with the system load, realize the line load and power balance, so as to help the power department to formulate a reasonable and effective power generation and distribution plan, provide a safe basic environment for the stable development of the power system, avoid energy loss, and fully improve the economic level. Therefore, according to the research of power load prediction, this paper summarizes the principle and technology of BP neural network prediction algorithm, and makes a short-term load prediction model of the power system of the network, and makes prediction results and error analysis for the model with 10 and 20 hidden layers successively.

Downloads

Download data is not yet available.

References

Srivastava A K, Pandey A S, Singh D. Short-term load forecasting methods: A review[C]. Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES), International Conference on. IEEE, 2016: 130-138.

Friedrich L, Afshari A. Short-term forecasting of the Abu Dhabi electricity load using multiple weather variables[J]. Energy Procedia, 2015, 75: 3014-3026.

Koo B, Kim H, Lee H, et al. Short-term Electric Load Forecasting for Summer Season using Temperature Data[J]. The Transactions of The Korean Institute of Electrical Engineers, 2015, 64(8): 1137-1144.

Dong D A I, Lin-chao M A. The Power Load Prediction based on the Improved Genetic Algorithm Optimized Neural Network[J]. Journal of Convergence Information Technology, 2013, 8(6).

Skolthanarat S, Lewlomphaisarl U, Tungpimolrut K. Short-term load forecasting algorithm and optimization in smart grid operations and planning[C]. Technologies for Sustainability (SusTech), 2014 IEEE Conference on. IEEE, 2014: 165-171.

Liu L, Chen J, Xu L. Realization and application research of BP neural network based on MATLAB[C] //2008 International Seminar on Future BioMedical Information Engineering. IEEE, 2008:130-133.