Dynamic mean absolute error as new measure for assessing forecasting errors
Fecha
2018Versión
Acceso cerrado / Sarbide itxia
Tipo
Artículo / Artikulua
Impacto
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10.1016/j.enconman.2018.02.030
Resumen
Accurate wind power forecast is essential for grid integration, system planning, and electricity trading in certain electricity markets. Therefore, analyzing prediction errors is a critical task that allows a comparison of prediction models and the selection of the most suitable model. In this work, the temporal error and absolute magnitude error are simultaneously considered to assess the foreca ...
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Accurate wind power forecast is essential for grid integration, system planning, and electricity trading in certain electricity markets. Therefore, analyzing prediction errors is a critical task that allows a comparison of prediction models and the selection of the most suitable model. In this work, the temporal error and absolute magnitude error are simultaneously considered to assess the forecast error. The trade-off between both types of errors is computed, analyzed, and interpreted. Moreover, a new index, the dynamic mean absolute error, DMAE, is defined to measure the prediction accuracy. This index accounts for both error components: temporal and absolute. Real cases of wind energy forecasting are used to illustrate the use of the new DMAE index and show the advantages of this new index over other error indices. [--]
Editor
Elsevier
Publicado en
Energy Conversion and Management, 162, 176-188
Notas
Alojado según Res. CNEAI 5/12/23 (ANECA)
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC