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Information Journal Paper

Title

EFFICIENT SHORT-TERM ELECTRICITY LOAD FORECASTING USING RECURRENT NEURAL NETWORKS

Pages

  46-54

Keywords

SHORT TERM LOAD FORECASTING (STLF) 
RECURRENT NEURAL NETWORK (RNN) 

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.

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    APA: Copy

    MANSOURI, VAHID, & AKBARI, MOHAMMAD E.. (2014). EFFICIENT SHORT-TERM ELECTRICITY LOAD FORECASTING USING RECURRENT NEURAL NETWORKS. JOURNAL OF ARTIFICIAL INTELLIGENCE IN ELECTRICAL ENGINEERING, 3(9), 46-54. SID. https://sid.ir/paper/333823/en

    Vancouver: Copy

    MANSOURI VAHID, AKBARI MOHAMMAD E.. EFFICIENT SHORT-TERM ELECTRICITY LOAD FORECASTING USING RECURRENT NEURAL NETWORKS. JOURNAL OF ARTIFICIAL INTELLIGENCE IN ELECTRICAL ENGINEERING[Internet]. 2014;3(9):46-54. Available from: https://sid.ir/paper/333823/en

    IEEE: Copy

    VAHID MANSOURI, and MOHAMMAD E. AKBARI, “EFFICIENT SHORT-TERM ELECTRICITY LOAD FORECASTING USING RECURRENT NEURAL NETWORKS,” JOURNAL OF ARTIFICIAL INTELLIGENCE IN ELECTRICAL ENGINEERING, vol. 3, no. 9, pp. 46–54, 2014, [Online]. Available: https://sid.ir/paper/333823/en

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