Short-term Power Load Forecasting Method of Data Center Considering PUE
In order to accurately predict the short-term power load of data centers, a short-term load forecasting model based on long- and short-term memory neural networks is proposed, which effectively compensates for the shortcomings of feed forward neural networks that cannot process the correlation infor...
Saved in:
Main Authors: | WU Jin-song, ZHANG Shao feng, XU Xiang-min, LI Shu-tao, HUANG Yong, LIAO Xiao |
---|---|
Format: | Article |
Language: | Chinese |
Published: |
Harbin University of Science and Technology Publications
2021-12-01
|
Series: | Journal of Harbin University of Science and Technology |
Subjects: | |
Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2028 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hybrid Optimization based on Deep Learning Approach for Short-Term Load Forecast of Electricity Demand in Buildings
by: Charan Sekhar Makula, et al.
Published: (2024-06-01) -
A Methodology for Electricity Demand Forecasting Using a Hybrid Approach
by: Fanidhar Dewangan, et al.
Published: (2025-01-01) -
Forecasting Stock Returns Using Long Short-Term Memory (LSTM) Model Based on Inflation Data and Historical Stock Price Movements
by: Nur Faid Prasetyo, et al.
Published: (2025-05-01) -
Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting
by: Mustapha Ez-zaiym, et al.
Published: (2025-06-01) -
Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting
by: Wenting Zhao, et al.
Published: (2025-06-01)