6533b7dbfe1ef96bd127098f
RESEARCH PRODUCT
Dynamic mean absolute error as new measure for assessing forecasting errors
Fermin MallorLaura Frías-paredesMartín Gastón-romeoTeresa Leónsubject
Absolute magnitudeWind powerIndex (economics)Renewable Energy Sustainability and the EnvironmentComputer sciencebusiness.industry020209 energyWork (physics)Energy Engineering and Power Technology02 engineering and technology021001 nanoscience & nanotechnologyGridMeasure (mathematics)Fuel TechnologyNuclear Energy and EngineeringStatistics0202 electrical engineering electronic engineering information engineeringElectricity0210 nano-technologybusinessPredictive modellingdescription
Abstract 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-04-01 | Energy Conversion and Management |