A Literature Review of Wind Forecasting Methods

In this paper, an overview of new and current developments in wind forecasting is given where the focus lies upon principles and practical implementations. High penetration of wind power in the electricity system provides many challenges to the power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help the power system operators reduce the risk of unreliability of electricity supply. This paper gives a literature survey on the categories and major methods of wind forecasting. Based on the assessment of wind speed and power forecasting methods, the future development direction of wind forecasting is proposed.

A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows.Traditional approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts.The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.

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 fore-casting 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 selec-tion.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.

Multi-model ensemble forecasting of 10-m wind speed over eastern China based on machine learning optimization

风对人类活动和电力运行有重大影响,准确预报短期风速具有深远的社会和经济意义.基于中国东部100个站点,本研究首先评估了5个业务模式对10米风速的预报能力,日本气象厅JMA模式在减少预报误差方面表现最好.进一步,利用5种数值模式和多种机器学习方法,将动力和统计相结合,对每个站点分别进行了特征工程和机器学习算法优选,建立了10米风速多模式集成预报模型。针对24至96小时预报时长,将该方法的预报性能与基于岭回归的多模式集成和JMA单模式进行比较.结果表明,基于机器学习优选的多模型集成方法可以将JMA模式的预报误差降低39%以上,预报效果的提升在11月最明显.此外,该方法优于基于岭回归的多模式集成方法.

Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model

Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms.

System bias correction of short-term hub-height wind forecasts using the Kalman filter

Wind energy is a fluctuating source for power systems, which poses challenges to grid planning for the wind power industry. To improve the short-term wind forecasts at turbine height, the bias correction approach Kalman filter (KF) is applied to 72-h wind speed forecasts from the WRF model in Zhangbei wind farm for a period over two years. The KF approach shows a remarkable ability in improving the raw forecasts by decreasing the root-mean-square error by 16% from 3.58 to 3.01 m s−1, the mean absolute error by 14% from 2.71 to 2.34 m s−1, the bias from 0.22 to − 0.19 m s−1, and improving the correlation from 0.58 to 0.66. The KF significantly reduces random errors of the model, showing the capability to deal with the forecast errors associated with physical processes which cannot be accurately handled by the numerical model. In addition, the improvement of the bias correction is larger for wind speeds sensitive to wind power generation. So the KF approach is suitable for short-term wind power prediction.

Short-Term Wind Power Prediction Based on ICEEMDAN-SE-LSTM Neural Network Model with Classifying Seasonal

Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and(long short-term memory)LSTM neural network is proposed and studied.First,the original data is prepossessed including removing outliers and filling in the gaps.Then,the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model.In addition,this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons.On this basis,sample entropy is used to calculate the complexity of each component and reconstruct them into trend components,oscillation components,and random components.Then,these three components are input into the LSTM neural network,respectively.Combined with the predicted values of the three components,the overall power prediction results are obtained.The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57%to 9.46%than other traditional models,which indicates the reliability and effectiveness of the proposed method for power prediction.

Short Term Wind Power Forecasting Using Autoregressive Integrated Moving Average Approach

Wind energy is one of the most promising electricity generating sources as a clean and free alternate compared with the conventional power plants and due to the volatility feature in the wind speeds it will reflect some problems in power systems reliability particularly if the system is deeply penetrated by wind farms. Therefore, wind power forecasting issue become and is still an important scope that will help in ED （economic dispatch）, UC （unit commitment） purposes to get more reliable and economic systems. This paper introduces short term wind power forecasting model, based on ARIMA （autoregressive integrated moving average） which will be applied to hourly wind data from Zaafarana 5 project in Egypt. The proposed model successfully outperforms the persistence model with significant improvement up to 6 h ahead.

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.

Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm

Wind speed forecasting is of great importance for wind farm management and plays an important role in grid integration. Wind speed is volatile in nature and therefore it is difficult to predict with a single model. In this study, three hybrid multi-step wind speed forecasting models are developed and compared — with each other and with earlier proposed wind speed forecasting models. The three models are based on wavelet decomposition(WD), the Cuckoo search(CS) optimization algorithm, and a wavelet neural network(WNN). They are referred to as CS-WD-ANN(artificial neural network), CS-WNN, and CS-WD-WNN, respectively. Wind speed data from two wind farms located in Shandong, eastern China, are used in this study. The simulation result indicates that CS-WD-WNN outperforms the other two models, with minimum statistical errors. Comparison with earlier models shows that CS-WD-WNN still performs best, with the smallest statistical errors. The employment of the CS optimization algorithm in the models shows improvement compared with the earlier models.

Wind Power Forecasting Methods Based on Deep Learning:A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid.Aiming to provide reference strategies for relevant researchers as well as practical applications,this paper attempts to provide the literature investigation and methods analysis of deep learning,enforcement learning and transfer learning in wind speed and wind power forecasting modeling.Usually,wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state,which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure,temperature,roughness,and obstacles.As an effective method of high-dimensional feature extraction,deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design,such as adding noise to outputs,evolutionary learning used to optimize hidden layer weights,optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting.The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness,instantaneity and seasonal characteristics.

Wind Speed Forecasting Based on ARMA-ARCH Model in Wind Farms

Wind speed forecasting is signif icant for wind farm planning and power grid operation. The research in this paper uses Eviews software to build the ARMA (autoregressive moving average) model of wind speed time series, and employs Lagrange multipliers to test the ARCH (autoregressive conditional heteroscedasticity) effects of the residuals of the ARMA model. Also, the corresponding ARMA-ARCH models are established, and the wind speed series are forecasted by using the ARMA model and ARMA-ARCH model respectively. The comparison of the forecasting accuracy of the above two models shows that the ARMA-ARCH model possesses higher forecasting accuracy than the ARMA model and has certain practical value.

Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

The present work reports the development of nonlinear time series prediction method of genetic algorithm（GA） with singular spectrum analysis（SSA） for forecasting the surface wind of a point station in the South China Sea（SCS） with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days（longer for some point station） in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.

Bias Correction in Wind Direction Forecasting Using the Circular-Circular Regression Method

Wind direction forecasting plays an important role in wind power prediction and air pollution management. Weather quantities such as temperature, precipitation, and wind speed are linear variables in which traditional model output statistics and bias correction methods are applied. However, wind direction is an angular variable; therefore, such traditional methods are ineffective for its evaluation. This paper proposes an effective bias correction technique for wind direction forecasting of turbine height from numerical weather prediction models, which is based on a circular-circular regression approach. The technique is applied to a 24-h forecast of 65-m wind directions observed at Yangmeishan wind farm, Yunnan Province, China, which consistently yields improvements in forecast performance parameters such as smaller absolute mean error and stronger similarity in wind rose diagram pattern.

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy

As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting.The methods for forecasting wind power and PV production.The physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production.Moreover,the experiments demonstrated that cloud map identification has a significant impact on PV generation.With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

Caribbean Sea Offshore Wind Energy Assessment and Forecasting

The exploitation of wind energy is rapidly evolving and is manifested in the ever-expanding global network of offshore wind energy farms.For the Small Island Developing States of the Caribbean Sea(CS),harnessing this mature technology is an important first step in the transition away from fossil fuels.This paper uses buoy and satellite observations of surface wind speed in the CS to estimate wind energy resources over the 2009–201911-year period and initiates hour-ahead forecasting using the long short-term memory(LSTM)network.Observations of wind power density(WPD)at the 100-m height showed a mean of approximately 1000 W/m^(2) in the Colombia Basin,though this value decreases radially to 600–800 W/m^(2) in the central CS to a minimum of approximately 250 W/m^(2) at its borders in the Venezuela Basin.The Caribbean Low-Level Jet(CLLJ)is also responsible for the waxing and waning of surface wind speed and as such,resource stability,though stable as estimated through monthly and seasonal coefficients of variation,is naturally governed by CLLJ activity.Using a commercially available offshore wind turbine,wind energy generation at four locations in the CS is estimated.Electricity production is greatest and most stable in the central CS than at either its eastern or western borders.Wind speed forecasts are also found to be more accurate at this location,and though technology currently restricts offshore wind turbines to shallow water,outward migration to and colonization of deeper water is an attractive option for energy exploitation.

Inferential Statistics and Machine Learning Models for Short-TermWind Power Forecasting

The inherent randomness,intermittence and volatility of wind power generation compromise the quality of the wind power system,resulting in uncertainty in the system’s optimal scheduling.As a result,it’s critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation.Inferred statistics are utilized in this research to infer general features based on the selected information,confirming that there are differences between two forecasting categories:Forecast Category 1(0-11 h ahead)and Forecast Category 2(12-23 h ahead).In z-tests,the null hypothesis provides the corresponding quantitative findings.To verify the final performance of the prediction findings,five benchmark methodologies are used:Persistence model,LMNN(Multilayer Perceptron with LMlearningmethods),NARX(Nonlinear autoregressive exogenous neural networkmodel),LMRNN(RNNs with LM training methods)and LSTM(Long short-term memory neural network).Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model,LMNN,NARX network,and LMRNN,and the 23-steps forecasting accuracy has improved by 19.61%.

ELMAN Neural Network with Modified Grey Wolf Optimizer for Enhanced Wind Speed Forecasting

The scope of this paper is to forecast wind speed. Wind speed, temperature, wind direction, relative humidity, precipitation of water content and air pressure are the main factors make the wind speed forecasting as a complex problem and neural network performance is mainly influenced by proper hidden layer neuron units. This paper proposes new criteria for appropriate hidden layer neuron unit’s determination and attempts a novel hybrid method in order to achieve enhanced wind speed forecasting. This paper proposes the following two main innovative contributions 1) both either over fitting or under fitting issues are avoided by means of the proposed new criteria based hidden layer neuron unit’s estimation. 2) ELMAN neural network is optimized through Modified Grey Wolf Optimizer (MGWO). The proposed hybrid method (ELMAN-MGWO) performance, effectiveness is confirmed by means of the comparison between Grey Wolf Optimizer (GWO), Adaptive Gbest-guided Gravitational Search Algorithm (GGSA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Cuckoo Search (CS), Particle Swarm Optimization (PSO), Evolution Strategy (ES), Genetic Algorithm (GA) algorithms, meanwhile proposed new criteria effectiveness and precise are verified comparison with other existing selection criteria. Three real-time wind data sets are utilized in order to analysis the performance of the proposed approach. Simulation results demonstrate that the proposed hybrid method (ELMAN-MGWO) achieve the mean square error AVG ± STD of 4.1379e-11 ± 1.0567e-15, 6.3073e-11 ± 3.5708e-15 and 7.5840e-11 ± 1.1613e-14 respectively for evaluation on three real-time data sets. Hence, the proposed hybrid method is superior, precise, enhance wind speed forecasting than that of other existing methods and robust.

Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting

Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-kNN-P that applies kNN on each decomposed intrinsic mode function (IMF) and residue for separate modelling and forecasting followed by summation and an EMD-kNN-M that forms a feature vector set from all IMFs and residue followed by a single kNN modelling and forecasting. These two configurations are compared with the persistent model and the conventional kNN model on a wind speed time series dataset from Singapore. The results show that the two EMD-kNN hybrid models have good performance for longer term forecasting and EMD-kNN-M has better performance than EMD-kNN-P for shorter term forecasting.