One Day Ahead Load Forecasting Using Recurrent Neural Network
نویسنده
چکیده
This paper presents short term load forecasting (STLF) in Java Island using recurrent neural network (RNN). The simple one of RNN is Elman, it has one hidden layer and suitable used in time series prediction. It can learn an input-output mapping which is nonlinear. The Elman RNN was proposed for one day a head forecasting, with interval time 30 minutes. Training model divided into weekday, weekend and holiday pattern. There are special day and holiday is depend on lunar, because this reason lunar become one of inputs. A set of variable have test using statistic analysis to get correlation and contribution. To get the feasibility of ERRN, it was compared with feed forward NN in the same structure. Simulation results show that the accuracy ERNN is better than FFNN. Minimum MAPE of ERNN is 1.07% and FFNN is 1.2%.
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