Open AccessWind power forecast using neural networks: Tuning with optimization techniques and error analysis
Author(s) -
Nazaré Gonçalo,
Castro Rui,
Gabriel Filho Luís R.A.
Publication year - 2020
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2460
Subject(s) - wind power , particle swarm optimization , wind speed , mean absolute percentage error , artificial neural network , wind power forecasting , benchmark (surveying) , electric power system , mean squared error , power (physics) , computer science , engineering , meteorology , artificial intelligence , machine learning , statistics , mathematics , geography , physics , quantum mechanics , electrical engineering , geodesy
The increased integration of wind power into the power system implies many challenges to the network operators, mainly due to the hard to predict and variability of wind power generation. Thus, an accurate wind power forecast is imperative for systems operators, aiming at an efficient and economical wind power operation and integration into the power system. This work addresses the issue of forecasting short‐term wind speed and wind power for 1 hour ahead, combining artificial neural networks (ANNs) with optimization techniques on real historical wind speed and wind power data. Levenberg‐Marquardt (LM) and particle swarm optimization (PSO) are used as training algorithms to update the weights and bias of the ANN applied to wind speed predictions. The forecasting performance produced by the proposed models are compared with each other, as well as with the benchmark persistence model. Test results show higher performance for ANN‐LM wind speed forecasting model, outperforming both ANN‐PSO and persistence. The application of ANN‐LM to wind power forecast revealed also a good performance, with an average improvement of 2.8% in relation to persistence. An innovative analysis of mean absolute percentage error (MAPE) behaviour in time and in typical days is finally offered in the paper.