Efficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks


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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