Enhancing Short-Term Power Load Forecasting for Industrial and Commercial Buildings: A Hybrid Approach Using TimeGAN, CNN, and LSTM

Enhancing Short-Term Power Load Forecasting for Industrial and Commercial Buildings: A Hybrid Approach Using TimeGAN, CNN, and LSTM

The application of smart meters was delayed, leading to sparse power load data collection in industrial and commercial buildings, often encompassing only days to a few months of data. In contrast, deep learning models necessitate extensive datasets, spanning several years. To bridge this data defici...

Full description

Bibliographic Details
Main Authors: Yushan Liu, Zhouchi Liang, Xiao Li
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Open Journal of the Industrial Electronics Society
Subjects:
Convolutional neural networks (CNNs)
generative adversarial network (GAN)
load forecasting
long short-term memory (LSTM)
time series
Online Access:https://ieeexplore.ieee.org/document/10262292/

Internet

https://ieeexplore.ieee.org/document/10262292/

Similar Items