AI Summary & Key Takeaways
AI InsightOverall, the study results suggest that the ANFIS model can be used as an effective tool to estimate average wind speed and power values in the study area.
📌 3 Key Takeaways
Abstract
An adaptive neuro-fuzzy inference systems (ANFIS) model was used for predicting regional average wind speed and power values in the Central Anatolian region of Turkey. In model development, longitude, latitude and altitude of wind stations and wind speed measurement height were taken as input variables, while wind speed and power values were taken as output variables for 4 different surface roughness characteristics. After a successful learning and training process the proposed model produced reasonable mean errors ranging from 0.19% to 2.89% and negligible root mean square errors in training and testing wind speed and wind power data. Overall, the study results suggest that the ANFIS model can be used as an effective tool to estimate average wind speed and power values in the study area.
Özet
An adaptive neuro-fuzzy inference systems (ANFIS) model was used for predicting regional average wind speed and power values in the Central Anatolian region of Turkey. In model development, longitude, latitude and altitude of wind stations and wind speed measurement height were taken as input variables, while wind speed and power values were taken as output variables for 4 different surface roughness characteristics. After a successful learning and training process the proposed model produced reasonable mean errors ranging from 0.19% to 2.89% and negligible root mean square errors in training and testing wind speed and wind power data. Overall, the study results suggest that the ANFIS model can be used as an effective tool to estimate average wind speed and power values in the study area.