Wind Energy Forecasting Research Articles

A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network

As the energy crisis intensifies, wind energy generated by wind turbines, commonly known as a promising renewable energy source, is being more frequently employed. As a result, wind energy forecasting, particularly wind speed forecasting, plays a crucial role for wind energy management. Due to their importance, many wind speed forecasting methods have been proposed. However, many of the traditional prediction models did not account for data pre-processing, or the constraints of an individual forecasting model, resulting in poor prediction accuracy. The purpose of this article is to present a unique forecasting model that incorporates noise-processing methods, statistical techniques, deep learning frameworks, and optimization algorithms to improve on existing methods. The suggested ensemble model was tested using 10-minute wind speed data from real-world conditions. The experimental results show that the mean absolute percentage error of the 10-minute prediction of the proposed model is 5.73%, which is also about 17% improvement compared to the competing model (mean absolute percentage error of 6.71%).

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.

Frequency Control Approach and Load Forecasting Assessment for Wind Systems

Frequency deviation has to be controlled in power generation units when there are fluctuations in system frequency. With several renewable energy sources, wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature. Whenever there is a mismatch between generation and demand, the frequency deviation may arise from the actual frequency 50 Hz (in India). To mitigate the frequency deviation issue, it is necessary to develop an effective technique for better frequency control in wind energy systems. In this work, heuristic Fuzzy Logic Based Controller (FLC) is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems. Frequency control is applied to reduce the frequency deviation due to fluctuations and load prediction information using ANN (Artificial Neural Network) and SVM (Support Vector Machine) learning models. The performance analysis of the proposed method is done with different machine learning based approaches. The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy. Simulation results show that the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Normalized Mean Absolute Error (NMAE) values are scaled down by 41.1%, 9.9% and 23.1% respectively in the proposed method while comparing with existing wavelet and BPN based approach.

Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

Wind speed forecasting is important for wind energy forecasting. In the modern era, the increase in energy demand can be managed effectively by forecasting the wind speed accurately. The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty, the curse of dimensionality, overfitting and non-linearity issues. The curse of dimensionality and overfitting issues are handled by using Boruta feature selection. The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory (Bi-LSTM). In this paper, Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed forecasting. The model identifies relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection (BFS). Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time steps. The proposed BFS-Bi-LSTM model is compared against Multilayer perceptron (MLP), MLP with Boruta (BFS-MLP), Long Short Term Memory (LSTM), LSTM with Boruta (BFS-LSTM) and Bi-LSTM in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and R2. The BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784, MAE of 0.530, MSE of 0.615 and R2 of 0.8766. The experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain.

Solar and Wind Energy Forecasting for Green and Intelligent Migration of Traditional Energy Sources

Fossil-fuel-based power generation leads to higher energy costs and environmental impacts. Solar and wind energy are abundant important renewable energy sources (RES) that make the largest contribution to replacing fossil-fuel-based energy consumption. However, the uncertain solar radiation and highly fluctuating weather parameters of solar and wind energy require an accurate and reliable forecasting mechanism for effective and efficient load management, cost reduction, green environment, and grid stability. From the existing literature, artificial neural networks (ANN) are a better means for prediction, but the ANN-based renewable energy forecasting techniques lose prediction accuracy due to the high uncertainty of input data and random determination of initial weights among different layers of ANN. Therefore, the objective of this study is to develop a harmony search algorithm (HSA)-optimized ANN model for reliable and accurate prediction of solar and wind energy. In this study, we combined ANN with HSA and provided ANN feedback for its weights adjustment to HSA, instead of ANN. Then, the HSA optimized weights were assigned to the edges of ANN instead of random weights, and this completes the training of ANN. Extensive simulations were carried out and our proposed HSA-optimized ANN model for solar irradiation forecast achieved the values of MSE = 0.04754, MAE = 0.18546, MAPE = 0.32430%, and RMSE = 0.21805, whereas our proposed HSA-optimized ANN model for wind speed prediction achieved the values of MSE = 0.30944, MAE = 0.47172, MAPE = 0.12896%, and RMSE = 0.55627. Simulation results prove the supremacy of our proposed HSA-optimized ANN models compared to state-of-the-art solar and wind energy forecasting techniques.

Machine Learning for Wind Energy Analysis and Forecasting

Abstract: Better prediction designs for the contributes directly of renewables are essential to reducing the requirement for controlling the energy associated with traditional power plants. For the extremely variable wind power output to be successfully integrated into the power system, a reliable forecast is especially crucial. For a wind inclusion study in the western United States, we use data from the National Renewable Energy Laboratory (NREL) and focus on summary wind power projection in this chapter. Our approach immediately derives functional connections from data, in contrast to physical procedures that depend on exceedingly complex differential equations. By reprising the regression model as a regression issue, we explore several regression strategies including regression models, k-nearest peers, and support vector regression. In our experiments, we look at forecasts for both individual wind turbines and entire wind parks, proving the viability of a machine learning technique for short-term wind power projection

The impact of input data resolution on neural network forecasting models for wind and photovoltaic energy generation using time series data

AbstractAccurate photovoltaic (PV) and wind energy forecasting are crucial for grid stability and energy security. There are various modeling techniques and methods to design forecasting models, each leading to different accuracy. In this research, datasets were collected from a 546 kWp grid‐connected PV farm and a 2 MW wind turbine for one full year. These data were used to train and test artificial neural network models to forecast day‐ahead PV and wind energy utilizing time‐series input data with 15‐, 30‐, and 60‐min resolutions. The models were able to forecast the PV energy accurately, while the same models trained for wind showed poor performance. Higher input data resolutions lead to slightly better forecasting performance for the PV farm. Utilizing data with higher resolution can improve the forecast by 1%–5%. While for wind energy forecasting, the resolution has very minor effects, although the 30‐min resolution shows a slightly better forecasting performance.

Analysis for the prediction of solar and wind generation in India using ARIMA, linear regression and random forest algorithms

This work focused on the prediction of generation of renewable energy (solar and wind) using the machine learning ML algorithms. Prediction of generation are very important to design the better microgrids storage. The various ML algorithms are as logistic regression LR and random forest RA and the ARIMA, time series algorithms. The performance of each algorithm is evaluated using the mean absolute error, mean squared error, root mean squared error, and mean absolute percentage error. The MAE value for the ARIMA (0.06 and 0.20) model for solar and wind energy is very less as compared to RF (15.65 and 61.73) and LR (15.78 and 54.65) of solar and wind energy. Same with MSE and RMSE, the MSE and RMSE value for the ARIMA of solar energy model obtained is 0.01 and 0.08 and wind energy is 0.07 and 0.27 respectively. Comparative analysis of all of these matrices of each algorithm for both the dataset, we concluded that the ARIMA model is best fit for the forecasting of solar energy and wind energy.

SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for sustainable energy), a novel deep learning-based auto-selective approach and tool that, instead of generalizing a specific model for all climates, predicts the best performing deep learning model for global horizontal irradiance (GHI) forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets created through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyze the tool in great detail through a variety of metrics and means for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), the normalized versions of these three metrics (nMAE, nRMSE, nMAPE), forecast skill (FS), and relative forecasting error. The long short-term memory-autoencoder model (LSTM-AE) outperformed the other four forecasting models and achieved the best results (nMAE = nRMSE = nMAPE = 0.02). The LSTM-AE model is the most accurate in all weather conditions. Predictions for sunny days are more accurate than for cloudy days as well as for summer compared to winter. SENERGY can predict the best forecasting model with 81% accuracy. The proposed auto-selective approach can be extended to other research problems, such as wind energy forecasting, and to predict forecasting models based on different criteria such as the energy required or speed of model execution, different input features, different optimizations of the same models, or other user preferences.

Design and research of hybrid forecasting system for wind speed point forecasting and fuzzy interval forecasting

Wind energy forecasting and analysis are essential for the management and operation of wind farms. Precise point forecasting of wind speed can assist operators in achieving the optimal dispatching of the power grid and diminish the cost of wind energy production. Interval forecasting can provide an effective reference for determining the rotating reserve of wind farms. Most existing studies have focused on either point forecasting or interval forecasting, and cannot serve as a reference for ensuring the reliability and stability of the power grid. In this study, we designed a novel hybrid forecasting system that includes point forecasting, interval forecasting, and evaluation, to provide technical support for the optimal coordinated dispatching of the power grid. Point forecasting is performed using a data preprocessing model and combined model to achieve higher precision. Interval forecasting estimates the upper and lower limits of the wind speed, which is used to determine the rotating reserve of wind farms and can effectively improve the stability of the power grid. The performance of the forecasting system was evaluated through numerical simulations of the wind speed at four sites in the Penglai wind farm. The results revealed that the proposed hybrid system not only provides accurate wind speed forecasting for coordinated dispatching of the power grid but also serves as a reference for determining the grid rotation reserve.

Deep neural networks for the quantile estimation of regional renewable energy production

Wind and solar energy forecasting have become crucial for the inclusion of renewable energy in electrical power systems. Although most works have focused on point prediction, it is currently becoming important to also estimate the forecast uncertainty. With regard to forecasting methods, deep neural networks have shown good performance in many fields. However, the use of these networks for comparative studies of probabilistic forecasts of renewable energies, especially for regional forecasts, has not yet received much attention. The aim of this article is to study the performance of deep networks for estimating multiple conditional quantiles on regional renewable electricity production and compare them with widely used quantile regression methods such as the linear, support vector quantile regression, gradient boosting quantile regression, natural gradient boosting and quantile regression forest methods. A grid of numerical weather prediction variables covers the region of interest. These variables act as the predictors of the regional model. In addition to quantiles, prediction intervals are also constructed, and the models are evaluated using different metrics. These prediction intervals are further improved through an adapted conformalized quantile regression methodology. Overall, the results show that deep networks are the best performing method for both solar and wind energy regions, producing narrow prediction intervals with good coverage.

A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique

As an important part of system operation and wind power station planning, wind energy forecasting is significantly affected by the strong volatility, intermittency and variability of the wind speed sequence itself. Therefore, to improve forecast accuracy and stability, the complex new energy forecasting problem is analyzed, and a research plan including denoising processing, input data feature optimization, model optimization selection and error correction is proposed. Apply the wavelet soft thresholding algorithm (WSTD) to sequence denoising, remove redundant information, and determine the best predictive model and model input for the sequence based on a new model selection principle (MSP) and phase space reconstruction technique (PSR), simultaneously, introduce the bayesian optimization (BO) to design the optimal reference search strategy to improve the learning efficiency of the model. Then, in order to avoid losing prediction information, a decomposition-segment error correction (DSEC) technique is proposed to enhance the veracity and adaptability of wind speed prediction. Based on 3 different data, the improved percentage of mean absolute percentage error is 7.366%, 7.514% and 10.649% respectively compared with the best performing comparative model, which verifies the effectiveness and applicability of the proposed framework and can provide strong support for smart grid planning.

Characteristics of Wind Resources and Post-Project Evaluation of Wind Farms in Coastal Areas of Zhejiang

As the onshore wind farm technology matures, offshore wind energy has attracted increasing attention. Zhejiang has coastal areas with massive potential for wind resources because of its geographical location. Therefore, understanding the wind resources in these areas can lay a foundation for future development and utilization. On this basis, this study used the measured wind field data of a wind farm along the coast of Zhejiang from March 2014 to February 2015 and from March 2016 to February 2018 to investigate and compare the characteristics of wind energy resources, including average wind speed, Weibull shape and scale factors, wind direction variation, and wind energy density. Then, the capacity coefficient of a wind turbine predicted using the wind farm data was compared with the actual capacity coefficients of two wind turbines in the wind farm in 2019. Results revealed the following observations: The overall variations in the evaluation indicators followed clear patterns over the 3 years. For example, the main wind direction in the same season was the same, and the variations in the monthly average wind speed, the monthly wind power density, and the theoretical capacity factors were highly similar. The time-series data indicated that the difference in the indicators between summer and autumn was significantly larger than that between other seasons, with the maximum difference in monthly average wind speed of 1.46 times and the maximum difference in monthly wind power density of 1.5 times. The comparison results of the capacity coefficient showed that the theoretical and actual capacity coefficients were extremely close when the monthly average wind speed was less than 6 m/s, with the average difference being less than 9%. When the monthly average wind speed was greater than 6 m/s, the proximity between the theoretical and actual capacity coefficients was reduced, with an average difference of more than 9% and a maximum value of 28%. In general, the overall characteristics of wind resources in coastal areas of Zhejiang exhibited similar trends but fluctuated considerably in some months. Wind energy forecasts had significant discrepancies from the actual operation indicators of the wind farm when the wind speed was high.

Interval deep learning architecture with rough pattern recognition and fuzzy inference for short-term wind speed forecasting

In recent decades, wind power is rapidly becoming a significant energy resource due to environmental considerations. The accuracy of wind energy forecasts is closely dependent on the prediction of wind speed time series. In this paper, a novel solution for ultra-short-term and short-term wind speed forecasting is introduced. The proposed method consists of a novel real-valued Deep Belief Network (DBN) with a new Rough feature extraction layer (RFEL) and a Fuzzy Type II Inference System (FT2IS) for robust supervised regression. To learn meaningful unsupervised features from the underlying wind speed data, real-valued input units are computed to better approximate the wind data distribution compared to the existing deep learning models. The proposed differentiable RFEL can be applied to any neural network to efficiently extract noise invariant features. A Takagi-Sugeno-Kang (TSK) system with interval Gaussian membership functions is employed for the supervised forecasting task. The high generalization capacity of the proposed unsupervised feature learning model incorporated into the robust RFEL and FT2IS leads to accurate predictions for highly varying wind speed time series. Numerical results on the Western Wind Dataset reveal significant performance improvements compared to recently proposed Deep Learning Architectures (DLAs), including the DBN, Stacked Autoencoder (SAE), and hybrid methodologies that leverage backtracking and metaheuristic optimization.

A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management.

Energy is the source of economic growth, and energy consumption indicates the country's state of development. Energy engineering is a relatively new technical discipline. It is increasinglyconsidered as a significant step in meeting carbon reduction targets, which can produce a variety of appealing outcomes that are useful to humanity's evolution. Many countries have adopted national policies to decrease pollution by reducing fossil fuel use and increasing renewable energy usage by alleviating climate change (wind and solar, etc.). The ever-growing need for renewable sources has led to economic and technological problems, such as wind energy, essential for effective grid control, and the design of a wind project. Precise estimates offer network operators and power system designers vital information for the generation of an appropriate wind turbine and controlling demand and supply power. This work provides an in-depth study of the proliferation of artificial intelligence (AI) in the prediction of wind energy generation. The devices employed to calculate wind speed are examined and discussed, with a focus on studies recently published. This review's findings show that AI is being employed in power wind energy measurement and forecasts. When compared to individual systems, the hybrid AI system gives more accurate findings. The discussion also found that correct handling and calibration of the anemometer can increase predicting accuracy. This conclusion suggests that increasing the accuracy of wind forecasting can be accomplished by lowering equipment errors that measure the meteorological parameter and mitigate carbon emission.

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

A Review on Wind Power Forecasting Models for improved Renewable Energy Integration

This paper gives an overview of the research that has been done on wind power forecasting models, which may be used to aid in the optimum integration of Renewable Energy (RE) into electric power networks. Aside from the economic advantages, the variable nature of wind energy production has a variety of negative consequences for the electric grid system, including stability, reliability, and the capacity to plan for future operations, among other things. As a result, precise forecasting of wind power output is critical for grid stability and security, as well as for the promotion of large-scale wind power. To ensure the accuracy of wind energy forecasts, a variety of conventional, artificial intelligence, and hybrid methodologies have been developed. The simplicity and robustness of time-series-based methods have made them popular for forecasting applications. Artificial Neural Networks (ANNs) and Fuzzy Logic have recently been supported by several researchers for forecasting because of their flexibility. This review covers the performance of many wind power forecasting models that are classified according to their categories. It is also offered a critical examination of contemporary studies, which includes statistical model and machine learning models that are based on historical data. Aspects of this study that are taken into consideration include the advantages and disadvantages of various forecasting models, including hybrid models, as well as performance matrices used in assessing the forecasting model. In addition, the possible advantages of model optimization are examined in detail as well.

Wind energy forecasting by fitting predicted probability density functions of wind speed measurements

Wind energy forecasting by fitting predicted probability density functions of wind speed measurements

A comprehensive review on deep learning approaches in wind forecasting applications

The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real-world applications due to the strong stochastic characteristics of wind energy. In recent years, the application boost of deep learning methods provides new effective tools in wind forecasting. This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy. Featured approaches include time-series-based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto-encoder-based approaches. In addition, future development directions of deep-learning-based wind energy forecasting have also been discussed.