Temporal hierarchies with autocorrelation for load forecasting We propose three different estimators that take into account the autocorrelation structure when reconciling forecasts in a temporal hierarchy. Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. In previous studies, weights assigned to the forecasts were given by the structure of the hierarchy or the forecast error variances without considering potential autocorrelation in the forecast errors. Our first estimator considers the autocovariance matrix within each aggregation level. Since this can be difficult to estimate, we propose a second estimator that blends autocorrelation and variance information, but only requires estimation of the firstorder autocorrelation coefficient at each aggregation level. Our third estimator facilitates information sharing between aggregation levels using a sparse representation of the inverse autocorrelation matrix. We demonstrate the usefulness of the proposed estimators through an application to shortterm electricity load forecasting in different price areas in Sweden. We find that by taking account of the autocovariance when reconciling forecasts, accuracy can be significantly improved uniformly across all frequencies and areas. (Less)

Day ahead electricity price forecast by NARX model with LASSO based features selection The availability of accurate dayahead price forecasts is crucial to achieve an effective participation to electricity markets. Starting from available state of the art, we propose a forecast technique exploiting a nonlinear auto regressive model with exogenous input, including a feature selection mechanism based on the Least Absolute Shrinkage and Selection Operator (LASSO). The rationale behind such a choice is twofold. On the one hand, we aim to target potential increase of forecast accuracy by learning complex nonlinear mappings. On the other hand, we want to increase the interpretability of the resulting model and minimize the effort needed to properly set up the forecaster. A framework such as the LASSO, capable to selfextract features from spot price multivariate time series, might represent a very useful tool for industrial practitioners. Experiments have been performed on Italian market dataset, demonstrating that the proposed method can extract useful features and achieve robust performance. Moreover, we show how the proposed method can support interpretation of forecaster structure and it can reveal interesting correlations within the regression set.