scholarly journals A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting

2021 ◽  
Author(s):  
Jiehui Huang ◽  
Zhiwang Zhou ◽  
Chunquan Li ◽  
Zhiyuan Liao ◽  
Peter X. Liu
Keyword(s):  
Short Term Memory ◽  
Load Forecasting ◽  
Memory Model ◽  
Electric Load ◽  
Time Dimension ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Electric Load Forecasting

scholarly journals An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4875
Author(s):  
Dengyong Zhang ◽  
Haixin Tong ◽  
Feng Li ◽  
Lingyun Xiang ◽  
Xiangling Ding
Keyword(s):  
Short Term Memory ◽  
Load Forecasting ◽  
Temperature Factor ◽  
Memory Model ◽  
Electrical Load ◽  
Short Term ◽  
Term Memory ◽  
Forecasting Method ◽  
Electrical Load Forecasting ◽  
Long Short Term Memory

Ultra-short-term electrical load forecasting is an important guarantee for the safety and efficiency of energy system operation. Temperature is also an important factor affecting the changes in electric load. However, in different cases, the impact of temperature on load forecasting will vary greatly, and sometimes even lead to the decrease of forecasting accuracy. This often brings great difficulties to researchers’ work. In order to make more scientific use of temperature factor for ultra-short-term electrical load forecasting, especially to avoid the negative influence of temperature on load forecasting, in this paper we propose an ultra-short-term electrical load forecasting method based on temperature factor weight and long short-term memory model. The proposed method evaluates the importance of the current prediction task’s temperature based on the change magnitude of the recent load and the correlation between temperature and load, and therefore the negative impacts of the temperature model can be avoided. The mean absolute percentage error of proposed method is decreased by 1.24%, 1.86%, and 6.21% compared with traditional long short-term memory model, back-propagation neural network, and gray model on average, respectively. The experimental results demonstrate that this method has obvious advantages in prediction accuracy and generalization ability.

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

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