Demand forecasting is critical in managing the demand of electricity. Authors have used the data from Kaggle competition and developed a neural network model to predict the energy loads across different grids of the network. The results show that, neural network model was able to perform well in predicting the electricity load across the network grid (Busseti, Osband, & Wong, 2012).
The authors have studied the forecasting of financial price movements using feed forward and recurrent neural networks. Authors have developed an ANN model to predict the financial time series value. The model has considered the feed forward non-deep networks with more neurons and deep networks with fewer neurons for analysis. The developed model has generated better estimates than the reference methods (Widegren, 2017).