%0 Journal Article %1 Taylor2003Using %A Taylor, James W. %A Buizza, Roberto %D 2003 %J International Journal of Forecasting %K MySeclifirmWTwork demand electricitynetworks energy ensembles forecasting nwp %N 1 %P 57--70 %R 10.1016/s0169-2070(01)00123-6 %T Using weather ensemble predictions in electricity demand forecasting %U http://dx.doi.org/10.1016/s0169-2070(01)00123-6 %V 19 %X Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electricity demand. The results show that the average of the demand scenarios is a more accurate demand forecast than that produced using traditional weather forecasts. We use the distribution of the demand scenarios to estimate the demand forecast uncertainty. This compares favourably with estimates produced using univariate volatility forecasting methods.

@article{Taylor2003Using, abstract = {Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electricity demand. The results show that the average of the demand scenarios is a more accurate demand forecast than that produced using traditional weather forecasts. We use the distribution of the demand scenarios to estimate the demand forecast uncertainty. This compares favourably with estimates produced using univariate volatility forecasting methods.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Taylor, James W. and Buizza, Roberto}, biburl = {https://www.bibsonomy.org/bibtex/2bbd109c4de247cd7fe6cc534a86d9a89/pbett}, citeulike-article-id = {13935858}, citeulike-attachment-1 = {Taylor_Buizza_2003_postprint.pdf; /pdf/user/pbett/article/13935858/1064096/Taylor_Buizza_2003_postprint.pdf; 560bf1e659e3856758c2b69fc03c788e5c34eb61}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/s0169-2070(01)00123-6}, doi = {10.1016/s0169-2070(01)00123-6}, file = {Taylor_Buizza_2003_postprint.pdf}, interhash = {5ca56caad855774cfdfd4dedbdb955ca}, intrahash = {bbd109c4de247cd7fe6cc534a86d9a89}, issn = {01692070}, journal = {International Journal of Forecasting}, keywords = {MySeclifirmWTwork demand electricitynetworks energy ensembles forecasting nwp}, month = jan, number = 1, pages = {57--70}, posted-at = {2016-02-18 15:53:34}, priority = {2}, timestamp = {2021-02-25T18:07:19.000+0100}, title = {Using weather ensemble predictions in electricity demand forecasting}, url = {http://dx.doi.org/10.1016/s0169-2070(01)00123-6}, volume = 19, year = 2003 }