Regularization methods for the short-term forecasting of the Italian electric load
This work addresses short-term electric load forecasting for Italy's grid operators, offering incremental improvements through regularization and aggregation techniques.
The paper tackled the problem of forecasting Italy's 24-hour electric load profile using multitask learning with regularization methods to control complexity, achieving up to a 30% reduction in error metrics like mean absolute percentage error compared to the national operator Terna.
The problem of forecasting the whole 24 profile of the Italian electric load is addressed as a multitask learning problem, whose complexity is kept under control via alternative regularization methods. In view of the quarter-hourly samplings, 96 predictors are used, each of which linearly depends on 96 regressors. The 96x96 matrix weights form a 96x96 matrix, that can be seen and displayed as a surface sampled on a square domain. Different regularization and sparsity approaches to reduce the degrees of freedom of the surface were explored, comparing the obtained forecasts with those of the Italian Transmission System Operator Terna. Besides outperforming Terna in terms of quarter-hourly mean absolute percentage error and mean absolute error, the prediction residuals turned out to be weakly correlated with Terna, which suggests that further improvement could ensue from forecasts aggregation. In fact, the aggregated forecasts yielded further relevant drops in terms of quarter-hourly and daily mean absolute percentage error, mean absolute error and root mean square error (up to 30%) over the three test years considered.