OICC Press International Journal of Energy and Environmental Engineering 2251-6832 11 3 (September 2020) 2020 07 08 Artificially intelligent models for the site-specific performance of wind turbines 10.1007/s40095-020-00352-2 EN R. Veena Institute of Applied Data Analytics, Universiti Brunei Darussalam, Gadong, 1410, BN S. Mathew Faculty of Engineering and Science, University of Agder, Grimstad, 4879, NO M. I. Petra Institute of Applied Data Analytics, Universiti Brunei Darussalam, Gadong, 1410, BN Journal Article 2020 07 08 Abstract Power developed by the wind turbines, at different wind velocities, is a key information required for the successful design and efficient management of wind energy projects. Conventionally, for these applications, manufacturer’s power curves are used in estimating the velocity–power characteristics of the turbines. However, performance of the turbines under actual field environments may significantly differ from the manufacturer’s power curves, which are derived under ‘standard’ conditions. In case of existing wind projects with sufficient performance data, the velocity–power variations can better be defined using artificially intelligent models. In this paper, we compare the performance of four such models by applying them to a 2-MW onshore wind turbine. Models based on ANN, KNN, SVM and MARS were developed and tested using the SCADA data collected from the turbine. All the AI models performed significantly better than the manufacturer’s power curve. Among the AI methods, SVM-based predictions showed the highest accuracy. A site-specific performance curve for the turbine, based on the SVM model, is presented. Wider adaptability of this approach has been demonstrated by successfully implementing the model for a 3.6-MW wind turbine, working under offshore environment. Being “site-specific data” driven, the proposed models are more accurate and hence better choice for applications like short-term wind power forecasting and pro-diagnostics of wind turbines.