Abstract

Knowing the demand for electrical consumption beforehand is important for efficient energy programming policies that can help with climate change, life cycle-costs, and optimal primary resource extraction. In this paper, we propose a framework to improve forecasting performance of high frequency electrical consumption data. We use different models for each day of the week, and then compose them to obtain the total forecast. We apply both machine learning (Long-Short Term Memory network) and econometric models (AutoRegressive Integrated Moving Average and Holtz-Winters) that consider time dependence in the data comparing model performance. We find that a classical ARIMA model outperforms other models; however, in applying the proposed framework, LSTM manages to outperform all other models. The results are statistically significant as indicated by the Model Confidence Set test constructed for Mean Absolute Percentage Error and Mean Square Error.