Open AccessA Parallel Electrical Optimized Load Forecasting Method Based on Quasi-Recurrent Neural Network
Author(s) -
Caiming Yang,
Wenxing Wang,
Xinxin Zhang,
Qinhui Guo,
Tianyi Zhu,
Qian Ai
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/696/1/012040
Subject(s) - computer science , recurrent neural network , big data , artificial neural network , artificial intelligence , visualization , convolutional neural network , machine learning , deep learning , electric power system , data mining , power (physics) , physics , quantum mechanics
Based on massive power big data resources, this paper establishes a new model for short-term load forecasting based on quasi-recurrent neural network (QRNN). QRNN combines the structural advantages of recurrent neural network (RNN) and convolutional neural network (CNN). It takes advantage of RNN’s cyclic connections to deal with the temporal dependencies of the load series, while implementing parallel calculations in both timestep and minibatch dimensions like CNN. The paper detailly describes the design and construction of QRNN, as well as the pre-processing and training steps of the forecasting model. Then, the algorithm is deployed to the big data platform, and an integrated load prediction system integrating data extraction, offline training, online forecasting and data visualization is developed. Finally, the proposed model is compared with some widely used machine learning load forecasting models. The results show that the QRNN based method achieves better prediction accuracy, and greatly improves the computational efficiency of training and testing, which is more practical for real-time and large-scale load forecasting.