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Paper Information

Journal:   JOURNAL OF ARTIFICIAL INTELLIGENCE IN ELECTRICAL ENGINEERING   JUNE 2014 , Volume 3 , Number 9; Page(s) 46 To 54.
 
Paper: 

EFFICIENT SHORT-TERM ELECTRICITY LOAD FORECASTING USING RECURRENT NEURAL NETWORKS

 
 
Author(s):  MANSOURI VAHID*, AKBARI MOHAMMAD E.
 
* DEPARTMENT OF ELECTRICAL ENGINEERING, AHAR BRANCH, ISLAMIC AZAD UNIVERSITY, AHAR, IRAN
 
Abstract: 

Short term load forecasting (STLF) plays an important role in the economic and reliable operation of power systems. Electric load demand has a complex profile with many multivariable and nonlinear dependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. The proposed model is capable of forecasting next 24-hour load profile. The main feature in this network is internal feedback to highlight the effect of past load data for efficient load forecasting results. Testing results on the three year demand profile shows higher performance with respect to common feed forward back propagation architecture.

 
Keyword(s): SHORT TERM LOAD FORECASTING (STLF), RECURRENT NEURAL NETWORK (RNN), HOURLY LOAD FORECAST, LOAD DATA NORMALIZATION
 
 
References: 
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Citations: 
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+ Click to Cite.
APA: Copy

MANSOURI, V., & AKBARI, M. (2014). EFFICIENT SHORT-TERM ELECTRICITY LOAD FORECASTING USING RECURRENT NEURAL NETWORKS. JOURNAL OF ARTIFICIAL INTELLIGENCE IN ELECTRICAL ENGINEERING, 3(9), 46-54. https://www.sid.ir/en/journal/ViewPaper.aspx?id=509884



Vancouver: Copy

MANSOURI VAHID, AKBARI MOHAMMAD E.. EFFICIENT SHORT-TERM ELECTRICITY LOAD FORECASTING USING RECURRENT NEURAL NETWORKS. JOURNAL OF ARTIFICIAL INTELLIGENCE IN ELECTRICAL ENGINEERING. 2014 [cited 2021July31];3(9):46-54. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=509884



IEEE: Copy

MANSOURI, V., AKBARI, M., 2014. EFFICIENT SHORT-TERM ELECTRICITY LOAD FORECASTING USING RECURRENT NEURAL NETWORKS. JOURNAL OF ARTIFICIAL INTELLIGENCE IN ELECTRICAL ENGINEERING, [online] 3(9), pp.46-54. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=509884.



 
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