Efficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks
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Abstract:
Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis 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 commonfeed forward back propagation architecture.
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Journal title
volume 3 issue 9
pages 46- 53
publication date 2014-06-01
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