Open AccessResearch on the combined predicting model of short-term load in smart grid
Author(s) -
Liping Sun,
Lifang Wang,
Siwei Jing,
Peng Zhou,
Fangling Yao
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 - 10.1088/1742-6596/1549/5/052127
Subject(s) - term (time) , smart grid , computer science , artificial neural network , power grid , grid , genetic algorithm , stability (learning theory) , electric power system , data mining , power (physics) , reliability engineering , artificial intelligence , machine learning , engineering , mathematics , physics , geometry , quantum mechanics , electrical engineering
As an important basis of power grid planning and dispatching, short-term load predicting model with high accuracy is very important to ensure the efficient and reliable operation of power grid. In this paper, the influencing factors of the short-term load of smart grid are determined by the method of grey correlation analysis. BP, RBF and Elman neural network construct the single prediction model of the short-term load of smart grid. The single prediction model is weighted by GA genetic algorithm, and the combined prediction model of the short-term load of smart grid is constructed and verified by an example. The results show that the error of the combined prediction model can be kept at about 0.4%, which has higher prediction accuracy and stability.