Research on short-term power load forecasting based on LSTM

Zhikang Shi; Yonggeng Lu

doi:10.1117/12.2640019

23 May 2022 Research on short-term power load forecasting based on LSTM

Zhikang Shi, Yonggeng Lu

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 122540S (2022) https://doi.org/10.1117/12.2640019
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

In order to improve the short-term power load prediction results, this paper uses long short-term memory network to predict short-term power load. With the improvement of accuracy requirements, the traditional load forecasting does not consider problems such as time series and eigenvalues. This paper proposes a long short-term memory network model to predict load. First, the data set is processed, cleaned and normalized, and the data set is divided into training set and sample set. Then, a prediction model is built, and appropriate parameters and eigenvalues are selected for the model to study the impact on the short-term power load under the LSTM model. This paper uses the Short-term electricity load forecasting (Panama) dataset on the kaggle platform to verify the model, and uses multivariate and multistep to forecast short-term electricity load.

Citation Download Citation

Zhikang Shi and Yonggeng Lu "Research on short-term power load forecasting based on LSTM", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 122540S (23 May 2022); https://doi.org/10.1117/12.2640019

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