Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

2011 ◽  
Author(s):  
K. Nose-Filho ◽  
A. D. P. Lotufo ◽  
C. R. Minussi
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Load Forecasting ◽  
General Regression Neural Network ◽  
Short Term ◽  
Moving Average Filter ◽  
Short Term Load Forecasting ◽  
General Regression ◽  
Average Filter

Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network

2011 ◽  
Vol 26 (4) ◽  
pp. 2862-2869 ◽  
Author(s):  
Kenji Nose-Filho ◽  
Anna Diva Plasencia Lotufo ◽  
Carlos Roberto Minussi
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
General Regression Neural Network ◽  
Short Term ◽  
General Regression

Research of Load Forecasting Based on General Regression Neural Network

2013 ◽  
Vol 732-733 ◽  
pp. 926-929
Author(s):  
Cheng Ming Wu ◽  
Wei Wei Yao ◽  
Wen Jing Dong
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Barometric Pressure ◽  
Forecast Model ◽  
General Regression Neural Network ◽  
Weather Factors ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Complete Test ◽  
General Regression

Load forecasting technology is an important guarantee of the safe and steady operation in power system. Based on the analyzing and designing the network structure of GRNN, this paper sets an appropriate smoothing parameter and proposes a strategy for load forecasting under considering the weather factors. A short-term load forecasting model with the factors of temperature, humidity, wind speed, barometric pressure and rainfall is established. And the model can achieve the expected result after a complete test. Finally, comparing with the forecast model based on BP neural network, GRNN method shows the great superiority in power load forecasting.

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