Forecasting Wind Power Generation Research Articles

Attack-resilient framework for wind power forecasting against civil and adversarial attacks

Forecasting wind power generation accurately is crucial for reliable, economical, and efficient integrations in smart grids, promoting applications of cleaner energy sources. Although effective wind power forecasting methods exist, power grids still require resilient schemes enabling accurate predictions under cyber-attacks. This paper introduces civil attack (CA) and fast gradient sign method (FGSM) attacks to wind power forecasting to analyze their impacts with countermeasures. The impacts of CA and FGSM attacks on a deep learning-based forecasting method are evaluated, finding FGSM attacks more severe. Also, an attack identification and corrupted data replacement-based pre-processing robust framework is proposed, outperforming other countermeasures. To detect and classify attacks, random forest (RF) has outperformed extreme gradient boosting (XGBoost), decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN). Experimental results on two different zones during CA and FGSM attacks indicate that the decrease in accuracy can be up to 0.4103, 0.3152, and 0.1683 in terms of root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE), respectively. The proposed framework successfully achieves an accuracy of 0.1204, 0.0835, and 0.0145 for the worst case in terms of RMSE, MAE, and MSE, respectively, signifying its importance for academic and industrial applications.

Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data

Wind power is an important energy source that can be used to supply clean energy and meet current energy needs. Despite its advantages in terms of zero emissions, its main drawback is its intermittency. Deterministic approaches to forecast wind power generation based on the annual average wind speed are usually used; however, statistical treatments are more appropriate. In this paper, an intelligent statistical methodology to forecast annual wind power is proposed. The seasonality of wind is determined via a clustering analysis of monthly wind speed probabilistic distribution functions (PDFs) throughout n years. Subsequently, a methodology to build the wind resource typical year (WRTY) for the n+1 year is introduced to characterize the resource into the so-called statistical seasons (SSs). Then, the wind energy produced at each SS is calculated using its PDFs. Finally, the forecasted annual energy for the n+1 year is given as the sum of the produced energies in the SSs. A wind farm in Mexico is chosen as a case study. The SSs, WRTY, and seasonal and annual generated energies are estimated and validated. Additionally, the forecasted annual wind energy for the n+1 year is calculated deterministically from the n year. The results are compared with the measured data, and the former are more accurate.

Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts

To forecast wind power generation in the scale of years to decades, outputs from climate models are often used. However, one major limitation of the data projected by these models is their coarse temporal resolution—usually not finer than three hours and sometimes as coarse as one month. Due to the non-linear relationship between wind speed and wind power, and the long forecast horizon considered, small changes in wind speed can result in big changes in projected wind power generation. Our study indicates that the distribution of observed 10 min wind speed data is relatively well preserved using three- or six-hourly instantaneous values. In contrast, daily or monthly values, as well as any averages, including three-hourly averages, are almost never capable of preserving the distribution of the underlying higher resolution data. Assuming that climate models behave in a similar manner to observations, our results indicate that output at three-hourly or six-hourly temporal resolution is high enough for multi-decadal wind power generation forecasting. In contrast, wind speed projections of lower temporal resolution, or averages over any time range, should be handled with care.

Probabilistic Wind Speed Forecasting for Wind Turbine Allocation in the Power Grid

To face the growing electricity demand, several countries have adopted the solution of clean energy and use renewable energy sources (e.g., wind and solar) to reinforce the stability of the power network, especially during peak demand periods. Forecasting wind power generation is one of the important tasks for the network regulator. This paper deals with the probabilistic forecasting of hourly wind speed time series. In this approach, instead of evaluating a single-point forecast, an intraday interval prediction is provided, which allows modeling the probability distribution of the wind speed process at any specific hour. Practically, the quantification of uncertainty might be of particular interest for risk management purposes associated with wind power generation. The definition of interval prediction is based on the notion of conditional quantiles. In this paper, we introduce a new statistical approach, which deals with the nonstationarity behavior of the wind speed process, to define the conditional quantile predictor. The proposed approach was applied and evaluated on hourly wind speed processes. The suggested methodology provides accurate single-point forecasts using the conditional median as the predictor. Furthermore, the obtained hourly interval predictions are small and well adapted to the shape of the daily wind speed curves, which confirms the efficiency of the proposed approach.

A deep learning sequence model based on self-attention and convolution for wind power prediction

Renewable energy has garnered significant attention recently due to its sustainable nature and minimal environmental footprint. Among various sources, wind energy emerges as one of the most promising. However, its inherently unpredictable and irregular characteristics pose challenges to forecasting wind power generation. This study introduces a wind power prediction model that employs self-attention to capture long-range relationships and convolutional layers to understand the local temporal dynamics within time-series data. Unlike traditional deep learning sequence models, such as the recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), our method adeptly integrates both global and local insights. We validate the model’s efficacy through experiments on three datasets. The results consistently show our model’s superior performance over alternative methods. Further, we conduct comprehensive experiments to analyze our proposed model.

Comparison and Enhancement of Machine Learning Algorithms for Wind Turbine Output Prediction with Insufficient Data

As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid.

Optimized ensemble model for wind power forecasting using hybrid whale and dipper-throated optimization algorithms

Introduction: Power generated by the wind is a viable renewable energy option. Forecasting wind power generation is particularly important for easing supply and demand imbalances in the smart grid. However, the biggest challenge with wind power is that it is unpredictable due to its intermittent and sporadic nature. The purpose of this research is to propose a reliable ensemble model that can predict future wind power generation.Methods: The proposed ensemble model comprises three reliable regression models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM models. To boost the performance of the proposed ensemble model, the outputs of each model are optimally weighted to form the final prediction output. The ensemble models’ weights are optimized in terms of a newly developed optimization algorithm based on the whale optimization algorithm and the dipper-throated optimization algorithm. On the other hand, the proposed optimization algorithm is converted to binary to be used in feature selection to boost the prediction results further. The proposed optimized ensemble model is tested in terms of a dataset publicly available on Kaggle.Results and discussion: The results of the proposed model are compared to the other six optimization algorithms to prove the superiority of the proposed optimization algorithm. In addition, statistical tests are performed to highlight the proposed approach’s performance and effectiveness in predicting future wind power values. The results are evaluated using a set of criteria such as root mean square error (RMSE), mean absolute error (MAE), and R2. The proposed approach could achieve the following results: RMSE = 0.0022, MAE = 0.0003, and R2 = 0.9999, which outperform those results achieved by other methods.

Wind Forecast at Medium Voltage Distribution Networks

Due to the intermittent and variable nature of wind, Wind Power Generation Forecast (WPGF) has become an essential task for power system operators who are looking for reliable wind penetration into the electric grid. Since there is a need to forecast wind power generation accurately, the main contribution of this paper is the development, implementation, and comparison of WPGF methods in a framework to be used by distribution system operators (DSOs). The methodology applied comprised five stages: pre-processing, feature selection, forecasting models, post-processing, and validation, using the historical wind power generation data (measured at secondary substations) of 20 wind farms connected to the medium voltage (MV) distribution network in Portugal. After comparing the accuracy of eight different models in terms of their relative root mean square error (RRMSE), extreme gradient boosting (XGBOOST) appeared as the best-suited forecasting method for wind power generation. The best average RRMSE achieved by the proposed XGBOOST model for 1-year training (January–December of 2020) and 6 months forecast (January–June of 2021) corresponds to 13.48%, outperforming the predictions of the Portuguese DSO by 20%.

A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia

Renewable energies, such as solar and wind power, have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve the current energy crisis. Integrating wind energy into a large-scale electric grid presents a significant challenge due to the high intermittency and nonlinear behavior of wind power. Accurate wind power forecasting is essential for safe and efficient integration into the grid system. Many prediction models have been developed to predict the uncertain and nonlinear time series of wind power, but most neglect the use of Bayesian optimization to optimize the hyperparameters while training deep learning algorithms. The efficiency of grid search strategies decreases as the number of hyperparameters increases, and computation time complexity becomes an issue. This paper presents a robust and optimized long-short term memory network for forecasting wind power generation in the day ahead in the context of Ethiopia’s renewable energy sector. The proposal uses Bayesian optimization to find the best hyperparameter combination in a reasonable computation time. The results indicate that tuning hyperparameters using this metaheuristic prior to building deep learning models significantly improves the predictive performances of the models. The proposed models were evaluated using MAE, RMSE, and MAPE metrics, and outperformed both the baseline models and the optimized gated recurrent unit architecture.

Short-term Wind Power Prediction Method Based on UAV Patrol and Deep Confidence Network

At present, wind power has become the most promising energy supply. However, the intermittent and fluctuating wind power also poses a huge challenge to accurately adjust the electrical load. In order to find a method capable of forecasting wind power generation in a short period of time, we propose a short-term wind power generation forecasting method based on an optimized deep belief network approach. Based on GEFCom2012 competition dataset, by continuously tuning the parameters of the deep belief network for 15 sets of experiments, we obtained three optimal laboratory combinations: Experiment 4, Experiment 10, and Experiment 12. The results show that the R-squared values of Experiment 4, Experiment 10 and Experiment 12 are the highest, which are 0.955, 0.93 and 0.98, respectively. The average R-squared value of these three tuned experiments is 0.2342 higher than the average of the other 12 experiments. At the same time, it is concluded that when the learning frequency is low, the linear function can learn the most obvious features more directly; When the learning frequency is high, the nonlinear function can learn the internal latent features more directly.

A Cyber-Secure generalized supermodel for wind power forecasting based on deep federated learning and image processing

Accurate wind power forecasting is one of the most important operations within the economic dispatch problem to increase the performance of power and energy systems. Accordingly, this study proposes a cyber-resilient hybrid approach based on the Federated Learning and Convolutional Neural Network (CNN) procedure for short-term wind power generation forecasting in different regions of Iran. Generalizability, data independence, forecasting for regions where no training data is available, and preserving the security and privacy of data are prominent features of the proposed method. The federated network was designed with an architecture of 9 clients to perform the training process and extract the salient features from the data associated with each region in each client via the CNN technique. Then, the generalized global supermodel is produced based on the extracted features in each client to forecast the wind power in new and unknown regions such as Mahshahr, Bojnord, and Lootak that had no training data available and had no effect on global supermodel generation. Various scenarios were developed to test the robustness of the suggested methodology. In the first scenario, wind power forecasting is performed based on the suggested technique. In this scenario, the accuracy of the generalized supermodel to forecast wind power generation in each of the Mahshahr, Bojnord, and Lootak regions is 84%, 85%, and 74%, respectively. The second scenario models the scaling attack by changing the wind speed parameters to evaluate the performance of forecasting models against the data integrity attack. In this scenario, an evaluation of the forecast results based on various performance metrics is conducted highlighting the accuracy reduction of the forecast model, due to the damage caused by cyber-attacks on the input data. In the third scenario, the detection of cyber-attack is done based on the image processing-based technique. The presented results emphasize the accurate performance and high generalizability of the cyber-resilient global supermodel in forecasting wind power in various regions of Iran.

Forecasting Electricity Power Generation of Pawan Danavi Wind Farm, Sri Lanka, Using Gene Expression Programming

This paper presents the development of a wind power forecasting model based on gene expression programming (GEP) for one of the major wind farms in Sri Lanka, Pawan Danavi. With the ever-increasing demand for renewable power generation, Sri Lanka has started harnessing electricity from wind power. Though the initial establishment cost of wind farms is high, the analyses clearly showcased the economic sustainability of wind power generation in long term. In this context, forecasting the wind power generation at Sri Lankan wind farms is important in many ways. However, limited research has been carried out in Sri Lanka to predict the wind power generation against the changing climate. Therefore, to overcome this research gap, a model was developed to forecast wind power generation against two climatic factors, viz. on-site wind speed and ambient temperature. The results showcased the robustness and accuracy of the proposed GEP-based forecasting model (with R2 = 0.92, index of agreement = 0.98, and RMSE = 259 kW). Moreover, the results of the study were compared against three different forecasting models and found comparable in terms of the model accuracy. The GEP-based model is advantageous over machine learning techniques due to its capability in deriving a mathematical expression. As an acceptable relationship was found between wind power generation and climatic factors, the proposed model facilitates the future projection of wind power generations with forecasted climatic factors. Though the application of GEP in the field of wind power generation is reported in a few research publications, this is the first research in which GEP is employed to model the power generation with respect to weather indices. The proposed prediction model is advantageous than machine learning models as the relationship between the wind power and the weather indices can be expressed.

Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study

Wind power represents a promising source of renewable energies. Precise forecasting of wind power generation is crucial to mitigate the challenges of balancing supply and demand in the smart grid. Nevertheless, the major difficulty in wind power is its high fluctuation and intermittent nature, making it challenging to forecast. This study aims to develop efficient data-driven models to accurately forecast wind power generation. Crucially, the main contributions of this work are listed in the following major elements. Firstly, we investigate the performance of enhanced machine learning models to forecast univariate wind power time-series data. Specifically, we employed Bayesian optimization (BO) to optimally tune hyperparameters of the Gaussian process regression (GPR), Support Vector Regression (SVR) with different kernels, and ensemble learning (ES) models (i.e., Boosted trees and Bagged trees) and investigated their forecasting performance. Secondly, dynamic information has been incorporated in their construction to further enhance the forecasting performance of the investigated models. Specifically, we introduce lagged measurements to enable capturing time evolution into the design of the considered models. Furthermore, more input variables (e.g., wind speed and wind direction) are used to further improve wind prediction performance. Actual measurements from three wind turbines in France, Turkey, and Kaggle are used to verify the efficiency of the considered models. The results reveal the benefit of considering lagged data and input variables to better forecast wind power. The results also showed that the optimized GPR and ensemble models outperformed the other machine learning models.

Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.

Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

Wind power is one of the most efficient renewable resources without emissions. Nonetheless, it is difficult to exactly forecast wind power generation given historical power and wind speed information, the failure of which may cost the risk of large-scale outages. This article takes a close look at the artificial recurrent neural network framework in the application of wind power forecasting. More intelligent mechanisms using attention to capture spatial-temporal patterns within historical data are emphasized in this work and are shown to be state-of-the-art for short-term wind power forecasting. Our experiments at a wind farm in southeast Australia using only the historical wind power generation and wind speed records from ambient weather stations show that, e.g., 7.4750% in mean absolute error (MAE) and 0.3345 in the coefficient of variation in the root mean squared error (CV-RMSE) for half-hour-ahead prediction. To interpret how the three models under consideration—the long- and short-term time-series network (LSTNet), the temporal pattern attention-based long short-term memory (TPA-LSTM) and the dual-stage attention-based recurrent neural network (DA-RNN)—work, we visualize and analyze the details of the models so that further improvement can be made by combining the advantageous components of the models.

Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

The integration of renewable generation adds complexity to the operation of the power system due to its unpredictable characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal kriging and analog approaches are used to forecast wind power generation and used as the input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal kriging captures the spatial and temporal information available in the database to improve wind forecasts. We evaluate the performance of using the spatio-temporal kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results demonstrate that the use of kriging based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

Comparative performance of AI methods for wind power forecast in Portugal

AbstractBecause wind has a high volatility and the respective energy produced cannot be stored on a large scale because of excessive costs, it is of utmost importance to be able to forecast wind power generation with the highest accuracy possible. The aim of this paper is to compare 1‐h‐ahead wind power forecasts performance using artificial intelligence‐based methods, such as artificial neural networks (ANNs), adaptive neural fuzzy inference system (ANFIS), and radial basis function network (RBFN). The latter was implemented using three different learning algorithms: stochastic gradient descent (SGD), hybrid, and orthogonal least squares (OLS). The application dataset is the injected wind power in the Portuguese power systems throughout the years 2010–2014. The network architecture optimization and the learning algorithms are presented. An initial data analysis showed data seasonality; therefore, the wind power forecasts were performed according to the seasons of the year. The results showed that ANFIS was the best performer method, and ANN and RBFN‐OLS also showed strong performances. RBFN‐Hybrid and RBFN‐SGD performed poorly. In general, all methods outperformed persistence.

An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China

With rapid development of wind power in China, it has become an integral part of the energy structure, so it is of practical significance to forecast wind power generation accurately. As wind power generation in China experiences both an exponential increase trend and a seasonal fluctuation pattern, it cannot be accurately forecasted by traditional models. As the grey model GM(1,1) can capture an exponential growth trend and the Hodrick-Prescott filter is known for its capability of characterizing seasonality factors, this paper proposes a novel seasonal forecasting method that integrates the HP filter into the grey model GM(1,1). The proposed model is then applied to carry out an empirical analysis based on the seasonal wind power generation data between 2013 and 2019 in China. The forecasting results from the new model are then compared with three existing approaches. The comparison results indicate that the proposed model generally outperforms existing methods as it can well capture seasonal fluctuations in the data series. A further prediction of wind power generation in China is conducted into a future horizon of 2020 and 2021 by using our model, followed by a set of policy recommendations for further development of the wind power industry in China.

Error-Based Wind Power Prediction Technique Based on Generalized Factors Analysis with Improved Power System Reliability

As a green energy source, the use of wind has been rapidly growing in recent years. Whereas wind has complex and stochastic nature hence precise wind power predictions are essential for economic operation of the wind energy systems. For utilities, the rapid variations in wind power can generate serious problem of reliability reduction. The forecasting of wind power changes allows a utility to plan the connection and disconnection of wind power generation based on forecasting wind power generation and predicted load. In this paper, an environment friendly wind power prediction technique of variable-speed wind power system is proposed. The technique is employed from the prediction algorithm to create a prediction model to get accurate power. It is authenticated on the producer power curve of the variable-speed wind system. Additionally, the technique is used in average monthly wind power prediction and the outcomes show a huge improvement in prediction accuracy using the proposed method. Further, the likely value of rated wind speed for installed wind power system in Vishakhapatnam, Bhopal, Ahmedabad, Thiruvananthapuram, Bangalore, India, are also discussed. The empirical outcomes are compared with different wind forecast models and based on the root mean square error (RMSE), the proposed model gives the perfection in prediction accuracy compared to Gaussian Processes and Numerical Weather Prediction, Wind power prediction without adjustment, Wind power prediction with adjustment, support vector machine methods. Further, the developed model is used to evaluate the annual reliability indices by convolving the predicted generation with predicted load in the selected station.

Online distributed learning in wind power forecasting

Forecasting wind power generation up to a few hours ahead is of the utmost importance for the efficient operation of power systems and for participation in electricity markets. Recent statistical learning approaches exploit spatiotemporal dependence patterns among neighbouring sites, but their requirement of sharing confidential data with third parties may limit their use in practice. This explains the recent interest in distributed, privacy preserving algorithms for high-dimensional statistical learning, e.g. with auto-regressive models. The few approaches that have been proposed are based on batch learning. However, these approaches are potentially computationally expensive and do not allow for the accommodation of nonstationary characteristics of stochastic processes like wind power generation. This paper closes the gap between online and distributed optimisation by presenting two novel approaches that recursively update model parameters while limiting information exchange between wind farm operators and other potential data providers. A simulation study compared the convergence and tracking ability of both approaches. In addition, a case study using a large dataset from 311 wind farms in Denmark confirmed that online distributed approaches generally outperform existing batch approaches while preserving privacy such that agents do not have to actively share their private data.