A multiple time series-based recurrent neural network for short-term load forecasting

2017 ◽  
Vol 22 (12) ◽  
pp. 4099-4112 ◽  
Author(s):  
Bing Zhang ◽  
Jhen-Long Wu ◽  
Pei-Chann Chang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Load Forecasting ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Short Term ◽  
Short Term Load Forecasting

Short-Term Reactive Power Load Forecasting Using Multiple Time-Series Model

2003 ◽  
Vol 36 (20) ◽  
pp. 985-990 ◽  
Author(s):  
Jong-Man Cho ◽  
Jin-Hack Kim ◽  
Woo-Hyun Park ◽  
Yun-ho Lee ◽  
Jin-O Kim
Keyword(s):  
Time Series ◽  
Reactive Power ◽  
Load Forecasting ◽  
Time Series Model ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting

scholarly journals Layer Recurrent Neural Network based Power System Load Forecasting

2015 ◽  
Vol 16 (3) ◽  
pp. 423
Author(s):  
Nikita Mittal ◽  
Akash Saxena
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Load Forecasting ◽  
Absolute Error ◽  
Percentage Error ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Load Pattern ◽  
Facility Expansion ◽  
Large Distribution

This paper presents a straight forward application of Layer Recurrent Neural Network (LRNN) to predict the load of a large distribution network. Short term load forecasting provides important information about the system’s load pattern, which is a premier requirement in planning periodical operations and facility expansion. Approximation of data patterns for forecasting is not an easy task to perform. In past, various approaches have been applied for forecasting. In this work application of LRNN is explored. The results of proposed architecture are compared with other conventional topologies of neural networks on the basis of Root Mean Square of Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). It is observed that the results obtained from LRNN are comparatively more significant.

Sign in / Sign up

Export Citation Format

Share Document