scholarly journals A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest

Energies ◽  
2016 ◽  
Vol 9 (10) ◽  
pp. 767 ◽  
Author(s):  
Nantian Huang ◽  
Guobo Lu ◽  
Dianguo Xu
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Load Forecasting ◽  
Feature Selection Method ◽  
Selection Method ◽  
Short Term ◽  
Electricity Load ◽  
Electricity Load Forecasting

scholarly journals Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1140 ◽  
Author(s):  
Xin Gao ◽  
Xiaobing Li ◽  
Bing Zhao ◽  
Weijia Ji ◽  
Xiao Jing ◽  
...  
Keyword(s):  
Feature Selection ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Short Term ◽  
Model Based ◽  
Electricity Load ◽  
Electricity Load Forecasting

Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.

Analysis of temperature-sensitive on short-term electricity load forecasting

2020 ◽  
Author(s):  
Nguyen Xuan Tung ◽  
Nguyen Quang Dat ◽  
Tran Ngoc Thang ◽  
Vijender Kumar Solanki ◽  
Nguyen Thi Ngoc Anh
Keyword(s):  
Load Forecasting ◽  
Temperature Sensitive ◽  
Short Term ◽  
Electricity Load ◽  
Electricity Load Forecasting

scholarly journals ARTIFICIAL NEURAL NETWORK BASE SHORT-TERM ELECTRICITY LOAD FORECASTING: A CASE STUDY OF A 132/33KV TRANSMISSION SUB-STATION

2020 ◽  
Vol 10 (2) ◽  
pp. 200-205
Author(s):  
Isaac Adekunle Samuel ◽  
Segun Ekundayo ◽  
Ayokunle Awelewa ◽  
Tobiloba Emmanuel Somefun ◽  
Adeyinka Adewale
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Electricity Load ◽  
Artificial Neural ◽  
Electricity Load Forecasting
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