Forecasting hourly electricity load profile using neural networks

We present INN, a new approach for predicting the hourly electricity load profile for the next day from a time series of previous electricity loads. It uses an iterative methodology to make the predictions for the 24-hour forecasting horizon. INN combines an efficient mutual information feature selection method with a neural network forecasting algorithm. We evaluate INN using two years of electricity load data for Australia, Portugal and Spain. The results show that it provides accurate predictions, outperforming three state-of-the-art approaches (weighted nearest neighbor, pattern sequence similarity and iterative linear regression), and a number of baselines. INN is also more accurate and efficient than a non-iterative version of the approach. We also found that although the range of load values for the three countries is very different, the load curves show similar patterns, which resulted in more than 90% overlap in the selected lag variables.