Teaching–Learning-Based Functional Link Artificial Neural Network for Short-Term Electrical Load Forecasting

2019 ◽  
pp. 433-450
Author(s):  
Rudra Narayan Pandey ◽  
Sarat Mishra ◽  
Sudhansu Kumar Mishra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Electrical Load ◽  
Short Term ◽  
Functional Link ◽  
Electrical Load Forecasting ◽  
Artificial Neural ◽  
Teaching Learning

Heterogeneous artificial neural network for short term electrical load forecasting

1996 ◽  
Vol 11 (1) ◽  
pp. 397-402 ◽  
Author(s):  
A. Piras ◽  
A. Germond ◽  
B. Buchenel ◽  
K. Imhof ◽  
Y. Jaccard
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Electrical Load ◽  
Short Term ◽  
Electrical Load Forecasting ◽  
Artificial Neural

scholarly journals Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

2021 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
Kumilachew Chane ◽  
◽  
Fsaha Mebrahtu Gebru ◽  
Baseem Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Electric Utility ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

Artificial Neural Network Modelling for Short Term Load Forecasting

2021 ◽  
Author(s):  
Mahshooq Abdul Majeed ◽  
Soumya Mudgal ◽  
Lalit Tak ◽  
Janavi Popat ◽  
Harsh Kakadiya ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Short Term Load Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Modelling
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