Quantitative short-term estimation of wind speed using ground-based measurements for varying latitudes locations: Individualistic and hybrid statistical-machine learning-based approach

Abstract

The pre-feasibility analysis of a commercial wind turbine occupies a long-term testing duration ranging between two to 4 years. This exercise specifically requires accurate knowledge of wind speed for the tested location with its applications also in informed decision-making and optimization. Though approaches for estimation of wind speed exist, they remain truly unvalidated using larger sample of data for a non-trained location. Furthermore, consideration of ground-based true measurements for development of estimator are also a concerning fact. So, the objective of presented investigation is to formulate a short-term data driven model for day ahead prediction of wind speed covering wide range of latitudes from 14.27 ⁰N 74.44 ⁰E to 17.32 ⁰N 76.83 ⁰E. The model is trained with actual set of data obtained from ground-based measurement stations set up by IMD (Pune). Six different individualistic and ensemble hybrid models are formulated to accurately forecast day ahead wind speed applicable for regions in Karnataka, India. Developed individualistic and hybrid models are derived from the statistical and machine learning approaches like seasonal ARIMA (SARIMAX), ANN, and LSTM. Among the developed models, LSTM and ANN-based models resulted in better forecasting accuracy than SARIMAX and the hybrid models possessing a mean absolute error of 0.133 and 0.068, respectively, for validation dataset 1. The MSE for the same are 0.038 sq. km/h and 0.009 sq. km/h, respectively. Similarly, the value of MSE for proposed ANN and LSTM for validation dataset 2 are 0.034 sq. km/h and 0.033 sq. km/h, respectively, and they possess a MAE of 0.11 km/h and 0.0095 km/h, respectively. In addition, statistical significance testing using the Diebold–Mariano test and residual diagnostics were conducted to verify the robustness of the forecasting results. The formulated models are also compared with the reported literature-based models and as a result of comparison the proposed models reflect better accuracy. By leveraging both ground measurements and theoretical understanding, the proposed short-term estimator for wind speed seeks to improve the accuracy and can be employed for prediction of wind speed.

Keywords

ensemble hybrid model ground-based training input temporal validation varying latitudes and MSE

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