Long-term forecasting of daily electric power load is investigated. After log-transformation and detrending of the data, the residual variability is decomposed as the sum of a "potential term", accounting for seasonality and weekly periodicity, and intervention events accounting for consumption changes associated with holidays and other special events. The biperiodic potential term is modeled as a linear combination of basis functions obtained from the tensor product of 7-day and 365-day harmonics. The intervention events are modeled by searching for "similar dates" in the historical records. The new forecaster is tested through the prediction of the whole 2013 load profile based on historical data until December 31, 2012. The results prove the effectiveness of the proposed approach that achieves a Mean Absolute Percentage Error (MAPE) equal to 2.96% not far from state-of-art performances of one-dayahead short-term forecasters.

The calibration of some stochastic differential equation used to model spot prices in electricity markets is investigated. As an alternative to relying on standard likelihood maximization, the adoption of a fully Bayesian paradigm is explored, that relies on Markov chain Monte Carlo (MCMC) stochastic simulation and provides the posterior distributions of the model parameters. The proposed method is applied to one‐ and two‐factor stochastic models, using both simulated and real data. The results demonstrate good agreement between the maximum likelihood and MCMC point estimates. The latter approach, however, provides a more complete characterization of the model uncertainty, an information that can be exploited to obtain a more realistic assessment of the forecasting error. In order to further validate the MCMC approach, the posterior distribution of the Italian electricity price volatility is explored for different maturities and compared with the corresponding maximum likelihood estimates.

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