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Recent wind speed forecasting works have applied various time series decomposition methods to segregate a complex wind time series into linear and nonlinear components. Then, existing statistical t...

The wind resources have been estimated by using physical models, statistical models, and artificial intelligence models. Wind power calculation helps us measure the annual energy that will sustain the balance between electricity generation and electricity consumption. Wind speed plays a significant role in calculating wind power, due to which here we focus on wind speed prediction. In this paper, hybrid models for wind speed forecasting have been proposed. The hybrid models are formed by combining the time series decomposition technique, that is, discrete wavelet transform (DWT), with statistical models, that is, autoregressive integrated moving average (ARIMA) and generalized autoregressive score (GAS), respectively. These hybrid models are referred to as DWT-ARIMA and DWT-GAS. DWT decomposes the original series into sub-series. After that, statistical models are applied to each sub-series for prediction. In the end, aggregate the prediction results of each sub-series to get the final forecasted series. For experimentation purposes, statistical and hybrid models are applied to various datasets that are taken from the NREL repository. In our studies, the hybrid version demonstrates better results in terms of accuracy and complexity, which indicates superior performance in most cases compared to the existing statistical models.

This article presents the Recurrent Neural Network (RNN) and its Attention mechanism to develop forecasting models for renewable energy applications. In this study, wind speed and solar irradiance forecasting models have been developed as these two factors play a significant role in renewable energy production. The irregular nature of wind poses the challenge of accurate wind speed prediction, while solar irradiance forecasting can aid in the planning and deployment of solar power plants. In this paper, six RNN techniques, namely RNN, GRU, LSTM, Content-based Attention, Luong Attention, and Self-Attention based RNN are considered for forecasting the future values of wind speed and solar irradiance in particular geographical locations. The aim is the identification of the advantages, comparison, and importance of different recurrent neural network methods for forecasting models. All models are developed on the datasets of the National Renewable Energy Laboratory (NREL) and NASA’s Prediction of Worldwide Energy Resource (POWER).

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.

This work was developed for forecasting wind speed data by using various statistical models, which can further be utilized for the estimation of the annual energy production of commercial wind farms. The pattern of historical data available for modeling may be linear or nonlinear. For linear time series pattern, Autoregressive Integrated Moving Average (ARIMA), ARIMAX model by using exogenous variable X and Vector Autoregressive (VAR) models have been developed. In order to achieve good performance on the nonlinear pattern, Generalized Autoregressive Score (GAS), GAS with exogenous variable (GASX) model has been developed. Wind time series data taken from different site locations of the National Renewable Energy Laboratory (NREL) repository have been utilized to develop the models. In our investigation, it has been found that the VAR model performs the best in most of the cases since it includes more than one variable for the development of the model.

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.

This study uses the kernalised version of Least Mean Square (LMS) algorithm which offers a technique of taking sample-by-sample update for reproducing kernel Hilbert spaces (RKHS), named as KLMS. This study uses online learning as it works in a dynamic mode where dependencies of variables change with time. Generally all statistical models build a model with historical data and make future predictions on the basis of developed model which is time-invariant. Also for a non-stationary process, it is not possible to capture the characteristics of relationship between input and output through a single curve. This paper presents an online method that builds a curve which evolves over the time. After the proposed approach is applied to the real world wind power system, the non-stationary behaviour of wind has been observed. The results of the simulation shows that the proposed technique can dynamically trace the time-varying nature of wind power system.

The uncertainty in the time series data like wind speed, network traffic, stock price etc. makes the prediction of these data a very tedious task. In order to improve the performance of prediction, several models have been invented. In this paper, some of the models like autoregressive models and Holt-Winters have been discussed. Further, the various steps involved in obtaining the results and comparing the performance of above model have been examined.