Wind forecasting is a time series problem, can aide in estimating the annual energy production of potential wind farms. Seasonality and trend are the two significant components that characterize the wind time series data. Variability in trend and seasonal component affects the performance of most of the forecasting methods. Therefore, to simplify the wind forecasting technique, generally, nonlinear seasonal and trend components are eliminated from wind time series data. Accuracy depends on the application function that is applicable to eliminate the trend and seasonality. In this article, a hybrid approach for time series forecasting has been proposed. A clustering technique has been developed, which finds the clusters of time series data showing identical trend components. After finding the proper clusters of similar trend components, statistical methods, namely, autoregressive integrated moving average and generalized autoregressive score techniques, are applied to the individual cluster. In the end, resulting components are aggregated. The experiment shows that the cluster-based forecasting technique gives better performance as compared with existing statistical models.

Estimation of wind power generation for grid interface helps in calculation of the annual energy production, which maintains the balance between electricity production and its consumption. For this purpose, accurate wind speed forecasting plays an important role. In this paper, linear statistical predictive models such as autoregressive integrated moving average (ARIMA), generalized autoregressive score (GAS) model and a GAS model with exogenous variable x (GASX) have been applied for accurate wind speed forecasting. Along with this, a non-linear statistical predictive modelling technique called non-linear GASX (NLGASX) has been proposed and applied to model non-linear time-series data. Furthermore, the proposed NLGASX model is optimized using modelling techniques based on neural networks, namely Sigmoid, TANH, Softmax and RELU. The proposed optimized NLGASX model performs far better as compared with other models. Wind speed is also used as an input to wind power curve model for predicting the wind power. According to the predicted wind power the annual energy has been calculated.

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Wind energy plays an essential role in the generation process of sustainable energy, with a bright future. Therefore, predicting wind speed fluctuations and their output power plays a crucial role in electric power generation. The integration of wind power is based on the accuracy of wind speed and power prediction model. In this paper, a clustering algorithm is proposed based on the length of the trendlet components. After spotting the different clusters, one suitable cluster is selected for modeling using the panda’s correlation method. This paper uses specific ARIMA, Naive Forecast, and Holt Winter models to forecast the selected cluster. Here three hybrid models, namely, C-ARIMA, C-NAIVE Forecast, and C-Holt-Winter, are proposed for wind speed forecasting. The performances of the proposed models are evaluated using the mean absolute error (MAE) and root mean squared error (RMSE). The experiment outcomes show that the cluster-based forecasting technique (Hybrid models) improved performance compared with un-clustered forecasting techniques.