Forecasting Wind Speed Using Clustering of Trend-Based Time Series Data

Accurate forecasting of wind speed is crucial for the efficient operation of wind energy systems. As a time-series concern, wind forecasting may help determine how much electricity a proposed wind farm might produce annually. The majority of forecasting techniques perform differently depending on seasonal and trends variation. For this reason, time series data frequently have seasonal and nonlinear trend components eliminated in order to simplify wind forecasting approach. The application function used to remove the seasonality and trend determines accuracy. The proposed method begins by identifying and extracting underlying trends from historical wind speed data, segmenting the time series into distinct trend-based components. This paper proposes a hybrid method for predicting time series. A method for clustering data has been designed that identifies clusters of time series data with similar trend components. Statistical procedures, such as generalized autoregressive and autoregressive integrated moving average scoring approaches, are used to each individual cluster after the appropriate clusters of related trend components have been identified. Ultimately, the components that are made are combined. The datasets collected from the NREL site. The experiment demonstrates that when compared to current statistical approaches, the cluster-based forecasting approach performs better. This research makes contribution towards the field of renewable energy forecasting by providing a robust and scalable method for wind speed prediction, which can be integrated into existing energy management systems for improving the efficiency and stability of wind energy generation. The research paper examines the trend features of time series data of wind employing the suggested hybrid technique on wind forecasting. Performance calculated in terms of RMSE and MAE shows that the proposed technique succeeds as compared to other state of the art techniques.