Short-term power load forecasting based on TimeGAN-LSTM

Duanxu Liu; Bin Wu; Li Sun

doi:10.1117/12.2681167

23 May 2023 Short-term power load forecasting based on TimeGAN-LSTM

Duanxu Liu, Bin Wu, Li Sun

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264538 (2023) https://doi.org/10.1117/12.2681167
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Because the deep learning model is highly dependent on data, and the extraction effect of data sample features will directly affect the prediction accuracy. Based on this, in order to improve the accuracy of short-term power load forecasting, a load forecasting method based on time series generation antagonism network TimeGAN and short-term memory network LSTM is proposed for data enhancement. First, in order to optimize the effect of model feature extraction, the sample six-dimensional feature data is reconstructed into nine-dimensional feature data according to the date and weather characteristics. Then, analyze the correlation between historical data and sample distance, use TimeGAN model to enhance the data, and then reconstruct the data set. Finally, the prediction model of long and short-term memory network is created to import the reconstructed data to predict the electric load in the next 24 hours. The experimental results show that this method is superior to the prediction methods of CNN-LSTM, CNN-BiLSTM and LSTM models, and TimeGan-LSTM has higher prediction accuracy.

Citation Download Citation

Duanxu Liu, Bin Wu, and Li Sun "Short-term power load forecasting based on TimeGAN-LSTM", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264538 (23 May 2023); https://doi.org/10.1117/12.2681167

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available