Comparative Study of ARIMA, Artificial Neural Network and Wavelet Transform for Electricity Daily Demand Forecasting

Demand forecasting is key to the efficient management of electrical energy systems. A novel approach is proposed in this paper for daily electrical load forecasting by combining the wavelet transform and neural networks. The electrical load at any particular time is usually assumed to be a linear combination of different components. From the signal analysis point of view, load can also be considered as a linear combination of different frequencies. Every component of load can be represented by one or several frequencies. The process of the proposed approach first decomposes the historical load into an approximate part associated with low frequencies and several detail parts associated with high frequencies through the wavelet transform. Then, a multilayered feed forward Neural Network, trained by low frequencies is used to predict the approximate part and trained by high frequencies is used to predict the detail parts of the future load. Finally, the short term load is forecasted by summing the predicted approximate part and the detail parts. Next, we studied the models of ARIMA and multilayered feed forward Neural Network. Electricity daily load was predicted from 1 step until 10 steps forward and forecasted quantities by 3 models were compared by indicators of RMSE and MAPE. The results show the application of the wavelet transform in daily load forecasting is encouraging.