Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting

Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting

Massive electrical load exhibits many patterns making it difficult for forecast algorithms to generalise well. Most learning algorithms produce a better forecast for dominant patterns in the case of weekday consumption and otherwise for less dominant patterns in weekend and holiday consumption. In v...

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Bibliographic Details
Main Authors: Moses Amoasi Acquah, Yuwei Jin, Byeong-Chan Oh, Yeong-Geon Son, Sung-Yul Kim
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Convolutional neural network-gated recurrent unit (CNN-GRU)
feature engineering
k-means clustering
LightGBM classifier
sequence-to-sequence forecast
short-term load forecast (STLF)
Online Access:https://ieeexplore.ieee.org/document/10013668/

Internet

https://ieeexplore.ieee.org/document/10013668/

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