Open AccessShort-term Load Forecasting of PowerSystem Based on Improved Feedforward Neural Network
Author(s) -
Wenbo Zhao,
Xiaoqin Liu,
Chengyu Li,
Fengwei Zhang,
Qian Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/5/052095
Subject(s) - artificial neural network , computer science , robustness (evolution) , term (time) , feedforward neural network , feed forward , electric power system , power grid , grid , generalization , power (physics) , control theory (sociology) , artificial intelligence , control engineering , engineering , control (management) , mathematics , mathematical analysis , biochemistry , chemistry , physics , geometry , quantum mechanics , gene
Short-term load forecasting of power system is the foundation of safe and stable operation of power grid, at present, short-term load forecasting of power system is easily affected by external features, so it is difficult to extract load data features accurately. Aiming at the problem of nonlinear, high-dimensional and poor generalization ability of load data, the short-term load forecasting method based on flower pollination algorithm and feedforward neural network is proposed. Flower pollination algorithm is used to optimize the combination of feedforward neural network weight threshold to reduce the error and improve the robustness of the algorithm. Finally, the prediction results of similar algorithms and those of the proposed method are compared through prediction evaluation indexes. After comparison, it is found that the method in this paper can effectively improve the accuracy of load prediction, which is conducive to increasing the safety and economy of power grid.