Electrical Load Forecasting Using Artificial Neural Network: The Case Study of the Grid Inter-Connected Network of Benin Electricity Community (CEB)

2018 ◽  
Vol 11 (2) ◽  
pp. 471-481 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Koffi A. Dotche ◽  
Koffi-Sa Bedja
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Electrical Load ◽  
Connected Network ◽  
Electrical Load Forecasting ◽  
Artificial Neural

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 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

scholarly journals The Short-Term Load Forecasting Using an Artificial Neural Network Approach with Periodic and Nonperiodic Factors: A Case Study of Tai'an, Shandong Province, China

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jiuyun Sun ◽  
Huanhe Dong ◽  
Ya Gao ◽  
Yong Fang ◽  
Yuan Kong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Shandong Province ◽  
Neural Network Approach ◽  
Electricity System ◽  
Spring Festival ◽  
Electricity Load ◽  
Artificial Neural

Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai’an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy.

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