Improved Long Short-Term Memory Network Based Short Term Load Forecasting

2019 ◽  
Author(s):  
Jie Cui ◽  
Qiang Gao ◽  
Dahua Li
Keyword(s):  
Short Term Memory ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Short Term Load Forecasting ◽  
Memory Network ◽  
Long Short Term Memory

Short-Term Load Forecasting using Long Short-Term Memory Network on Various Sub System Load in PLN Indonesia

2021 ◽  
Author(s):  
Rio Indralaksono ◽  
Alif Maulana Firdaus ◽  
M. Abdul Wakhid ◽  
Novemi Uki Andreas ◽  
Galih Hendra Wibowo ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
System Load ◽  
Short Term Load Forecasting ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4121
Author(s):  
Shaoqian Pei ◽  
Hui Qin ◽  
Liqiang Yao ◽  
Yongqi Liu ◽  
Chao Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Dew Point ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Short Term Load Forecasting ◽  
Memory Network ◽  
Long Short Term Memory

Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. However, how to select the optimal feature set and accurately predict multi-step ahead short-term load still faces huge challenges. In this paper, a hybrid feature selection method is proposed, an Improved Long Short-Term Memory network (ILSTM) is applied to predict multi-step ahead load. This method firstly takes the influence of temperature, humidity, dew point, and date type on the load into consideration. Furthermore, the maximum information coefficient is used for the preliminary screening of historical load, and Max-Relevance and Min-Redundancy (mRMR) is employed for further feature selection. Finally, the selected feature set is considered as input of the model to perform multi-step ahead short-term load prediction by the Improved Long Short-Term Memory network. In order to verify the performance of the proposed model, two categories of contrast methods are applied: (1) comparing the model with hybrid feature selection and the model which does not adopt hybrid feature selection; (2) comparing different models including Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) using hybrid feature selection. The result of the experiments, which were developed during four periods in the Hubei Province, China, show that hybrid feature selection can improve the prediction accuracy of the model, and the proposed model can accurately predict the multi-step ahead load.

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