Enhancing Short-Term Power Load Forecasting for Industrial and Commercial Buildings: A Hybrid Approach Using TimeGAN, CNN, and LSTM-Reference-Cited by-同舟云学术

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

Author:

Liu Yushan¹^ORCID,Liang Zhouchi¹,Li Xiao¹^ORCID

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

Funder

National Natural Science Foundation of China

Beijing Nova Program

National Key Laboratory of Operation and Control of New Power Systems of Tsinghua University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Systems Engineering

Link

Reference39 articles.

4. ImageNet: Classification with deep convolutional neural networks;krizhevsky;Proc 26th Annu Conf Neural Inf Process Syst,2012

5. Hybrid techniques for short term load forecasting;panda;Majlesi J Elect Eng,2023

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