Forecasting short term electric load based on stationary output of artificial neural network considering sequential process of feature extraction methods

2012 ◽  
Author(s):  
M.M. Othman ◽  
M.H.H. Harun ◽  
I. Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Electric Load ◽  
Sequential Process ◽  
Short Term ◽  
Artificial Neural

scholarly journals Short Term Electric Load Forecasting of Kathmandu Valley of Nepal using Artificial Neural Network

2018 ◽  
Vol 1 (1) ◽  
pp. 43-48
Author(s):  
Binod Bhandari ◽  
Shree Raj Shakya ◽  
Ajay Kumar Jha
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Load Flow ◽  
Kathmandu Valley ◽  
Electric Load ◽  
Short Term ◽  
Load Demand ◽  
The Neural Network ◽  
Artificial Neural

Decision making in the energy sector has to be based on accurate forecasts of the load demand. Short-term forecasting, which forms the focus of this paper, gives a day ahead hourly forecast of electric load. This forecast can help to make important decisions in the field of scheduling, contingency analysis, load flow analysis, preventing imbalance in the power generation and load demand, load switching strategies, thus leading to greater network reliability and power quality. A method called Artificial Neural Network is used to anticipate the future load of Kathmandu Valley of Nepal. The Neural Network is build, trained with historical data along with seven different input variables and used for prediction of day ahead 24 hours load. The output is validated with the real Load collected from NEA. In addition, forecasting is performed by some other time series methods as well, and whose output are compared with that of neural network. The range of Mean Absolute Deviation for four different time series models lied between 1.50-2.59. When the errors were calculated in terms of MSE and MAPE the range of these values were found to be in between 2.59-7.78, and 1.61- 5.07 respectively. The Artificial Neural Network proved to be the more accurate forecast method when the results are compared in terms of error measurements with a MAD having 1.23, MSE having 1.79 and MAPE having 1.17. The Neural Network proved to be more accurate method comparatively with satisfactory minimum error.

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