Open AccessA per‐unit curve rotated decoupling method for CNN‐TCN based day‐ahead load forecasting
Author(s) -
He Shengtao,
Li Canbing,
Liu Xubin,
Chen Xinyu,
Shahidehpour Mohammad,
Chen Tao,
Zhou Bin,
Wu Qiuwei
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/gtd2.12214
Subject(s) - computer science , unit load , decoupling (probability) , convolutional neural network , algorithm , control theory (sociology) , mathematics , artificial intelligence , engineering , mechanical engineering , control (management) , control engineering
The existing load forecasting method based on the per‐unit curve static decoupling (PCSD) would easily lead to the deviation and translation of forecasting results. To tackle this challenge, a per‐unit curve rotated decoupling (PCRD) method is proposed for day‐ahead load forecasting with convolutional neural network and temporal convolutional network framework. The PCRD method decomposes the load into three parts: the rotated per‐unit load curve, the 0 AM load, and the daily average load. The shape feature of the load curve is extracted by CNN, the temporal features of the 0 AM load and daily average load are extracted by TCN. The rotation operation is to rotate the per‐unit load curve at the midpoint of the curve until the first load point is aligned to the same point, in order to improve the similarity of per‐unit load curves and to alleviate the deflection of forecasting results. The 0 AM load can verify the accuracy of the daily average load, which alleviates the translation of forecasting results. Several experimental results show that the proposed method has higher accuracy and stability than the existing PCSD method. After repeated experiments on multiple data sets, the generalization ability of the model is also verified.