Open AccessShort‐term load demand forecasting through rich features based on recurrent neural networks
Author(s) -
Zhao Dongbo,
Ge Qian,
Tian Yuting,
Cui Jia,
Xie Boqi,
Hong Tianqi
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12069
Subject(s) - computer science , artificial neural network , smart grid , term (time) , grid , sequence (biology) , artificial intelligence , renewable energy , time sequence , recurrent neural network , electric power system , machine learning , data mining , power (physics) , engineering , physics , geometry , mathematics , quantum mechanics , biology , electrical engineering , genetics
Abstract With the emerging penetration of renewables and dynamic loads, the understanding of grid edge loading conditions becomes increasingly substantial. Load modelling researches commonly consist of explicitly expressed load models and non‐explicitly expressed techniques, of which artificial intelligence approaches turn out to be the major path. This paper reveals the artificial intelligence‐based load modelling technique to enhance the knowledge of current and future load information considering geographical and weather dependencies. This paper presents a recurrent neural network based sequence to sequence (Seq2Seq) model to forecast the short‐term power loads. Also, a feature attention mechanism, which is along channel and time directions, is developed to improve the efficiency of feature learning. The experiments over three publicly available datasets demonstrate the accuracy and effectiveness of the proposed model.