%0 Journal Article %1 pezzulli2006seasonal %A Pezzulli, Sergio %A Frederic, Patrizio %A Majithia, Shanti %A Sabbagh, Sal %A Black, Emily %A Sutton, Rowan %A Stephenson, David %D 2006 %J Applied Stochastic Models in Business and Industry %K MySeclifirmWTwork demand energy ensembles forecasting model seasonal statistics %N 2 %P 113-125 %R https://doi.org/10.1002/asmb.622 %T The seasonal forecast of electricity demand: a hierarchical Bayesian model with climatological weather generator %U https://onlinelibrary.wiley.com/doi/abs/10.1002/asmb.622 %V 22 %X In this paper we focus on the one year ahead prediction of the electricity peak‐demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast.

@article{pezzulli2006seasonal, abstract = {In this paper we focus on the one year ahead prediction of the electricity peak‐demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. }, added-at = {2021-02-25T18:10:53.000+0100}, author = {Pezzulli, Sergio and Frederic, Patrizio and Majithia, Shanti and Sabbagh, Sal and Black, Emily and Sutton, Rowan and Stephenson, David}, biburl = {https://www.bibsonomy.org/bibtex/2953dc6a85a4c1985644b70be0d3f92bc/pbett}, doi = {https://doi.org/10.1002/asmb.622}, interhash = {290bf24d24e035783b2bc2bc0ac55041}, intrahash = {953dc6a85a4c1985644b70be0d3f92bc}, journal = {Applied Stochastic Models in Business and Industry }, keywords = {MySeclifirmWTwork demand energy ensembles forecasting model seasonal statistics}, number = 2, pages = {113-125}, timestamp = {2021-02-25T18:11:42.000+0100}, title = {The seasonal forecast of electricity demand: a hierarchical Bayesian model with climatological weather generator}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asmb.622}, volume = 22, year = 2006 }