A dynamic weighting ensemble approach for wind energy production prediction
- Resource Type
- Conference
- Authors
- Al-Dahidi, Sameer; Baraldi, Piero; Zio, Enrico; Legnani, Edoardo
- Source
- 2017 2nd International Conference on System Reliability and Safety (ICSRS) System Reliability and Safety (ICSRS), 2017 2nd International Conference on. :296-302 Dec, 2017
- Subject
- Power, Energy and Industry Applications
Computational modeling
Production
Predictive models
Wind forecasting
Training
Wind energy
wind plant
electric power reliability
energy production prediction
artificial neural networks
ensemble of models
- Language
In this work, we propose a method to predict wind energy production. The method is based on an ensemble of Artificial Neural Networks (ANNs), which receive in input weather forecast variables and predict the wind plant energy production. We investigate different strategies for aggregating the outcomes of the individual models of the ensemble and compare them with a real dataset. A dynamic weighting ensemble which combines the individual models outcomes proportionally to their local performances in the neighborhood of the test pattern under analysis is found to provide the most accurate predictions.