Neural networks ensembles for short-term load forecasting

This paper proposes a new approach for short-term load forecasting based on neural networks ensembling methods. A comparison between traditional statistical linear seasonal model and ANN-based models has been performed on the real-world building load data, considering the utilisation of external data such as the day of the week and building occupancy data. The selected models have been compared to the prediction of hourly demand for the electric power up to 24 hours for a testing week. Both neural networks ensembles achieved lower average and maximum errors than other models. Experiments showed how the introduction of external data had helped the forecasting.