Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy sys...Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.展开更多
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 refere...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.展开更多
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 ...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%.展开更多
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...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.展开更多
Wind power generation is among the most promising and eco-friendly energy sources today. Wind Power Forecasting (WPF) is essential for boosting energy efficiency and maintaining the operational stability of power grid...Wind power generation is among the most promising and eco-friendly energy sources today. Wind Power Forecasting (WPF) is essential for boosting energy efficiency and maintaining the operational stability of power grids. However, predicting wind power comes with significant challenges, such as weather uncertainties, wind variability, complex terrain, limited data, insufficient measurement infrastructure, intricate interdependencies, and short lead times. These factors make it difficult to accurately forecast wind behavior and respond to sudden power output changes. This study aims to precisely forecast electricity generation from wind turbines, minimize grid operation uncertainties, and enhance grid reliability. It leverages historical wind farm data and Numerical Weather Prediction data, using k-Nearest Neighbors for pre-processing, K-means clustering for categorization, and Long Short-Term Memory (LSTM) networks for training and testing, with model performance evaluated across multiple metrics. The Grey Wolf Optimized (GWO) LSTM classification technique, a deep learning model suited to time series analysis, effectively handles temporal dependencies in input data through memory cells and gradient-based optimization. Inspired by grey wolves’ hunting strategies, GWO is a population-based metaheuristic optimization algorithm known for its strong performance across diverse optimization tasks. The proposed Grey Wolf Optimized Deep Learning model achieves an R-squared value of 0.97279, demonstrating that it explains 97.28% of the variance in wind power data. This model surpasses a reference study that achieved an R-squared value of 0.92 with a hybrid deep learning approach but did not account for outliers or anomalous data.展开更多
Wind power forecasting(WPF)is important for safe,stable,and reliable integration of new energy technologies into power systems.Machine learning(ML)algorithms have recently attracted increasing attention in the field o...Wind power forecasting(WPF)is important for safe,stable,and reliable integration of new energy technologies into power systems.Machine learning(ML)algorithms have recently attracted increasing attention in the field of WPF.However,opaque decisions and lack of trustworthiness of black-box models for WPF could cause scheduling risks.This study develops a method for identifying risky models in practical applications and avoiding the risks.First,a local interpretable model-agnostic explanations algorithm is introduced and improved for WPF model analysis.On that basis,a novel index is presented to quantify the level at which neural networks or other black-box models can trust features involved in training.Then,by revealing the operational mechanism for local samples,human interpretability of the black-box model is examined under different accuracies,time horizons,and seasons.This interpretability provides a basis for several technical routes for WPF from the viewpoint of the forecasting model.Moreover,further improvements in accuracy of WPF are explored by evaluating possibilities of using interpretable ML models that use multi-horizons global trust modeling and multi-seasons interpretable feature selection methods.Experimental results from a wind farm in China show that error can be robustly reduced.展开更多
Wind power forecasting(WPF)accuracy is fundamentally constrained by two critical challenges.First,the high-order nonlinear relationship between wind speed(WS)and power(WP)substantially amplifies errors inherent in num...Wind power forecasting(WPF)accuracy is fundamentally constrained by two critical challenges.First,the high-order nonlinear relationship between wind speed(WS)and power(WP)substantially amplifies errors inherent in numerical weather prediction(NWP)data.Second,conventional models process all input features uniformly,failing to distinguish the dominant role of the primary driving feature from the complementary roles of auxiliary features.To decouple and address these challenges,this paper proposes a novel forecasting method(CFRM-DCM)that integrates a Correction Factor Representation Model(CFRM)and a Dual-Channel Mechanism(DCM).The CFRM is first employed to address the NWP error.It describes the complex correlation and forecasting error between measured WS and NWP WS as correction factors.A generative adversarial network(GAN)is then utilized to learn the distribution of these factors and output a corrected,more accurate WS.This corrected data is then fed into the DCM,a dual-branch architecture designed to enhance complex feature extraction,overcoming the limitations of traditional single-channel structures.The proposed method is validated on four wind farms.Simulation results demonstrate that the CFRM-DCM method achieves significant improvements in WPF accuracy,with error reductions ranging from 3.9%to 9.4%across ultra-short-term and short-term timescales.This enhanced WPF performance is directly attributed to the model’s ability to first improve WS accuracy,with gains of 8.8%,7.6%,8.3%,and 8.8%for the respective farms.展开更多
The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward...The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.展开更多
Accurate predictions of wind power generation several months in advance are crucial for the effective operation and maintenance of wind farms and for facilitating efficient power purchase planning.This study evaluates...Accurate predictions of wind power generation several months in advance are crucial for the effective operation and maintenance of wind farms and for facilitating efficient power purchase planning.This study evaluates the performance of the seasonal prediction system of the National Centre for Medium-Range Weather Forecasting in forecasting near-surface winds.An analysis of 23 years of hindcast data,from 1993 to 2015,indicates that the seasonal prediction system effectively captures the inter-annual variability of near-surface winds.Specifically,predictions initialized in May demonstrate notable accuracy,with a skill score of 0.78 in predicting the sign of wind speed anomalies aggregated across various wind farms during the high wind season(June to August).Additionally,we critically examine the peculiarity of a case study from 2020,when the Indian wind industry experienced low power generation.To enhance forecasting accuracy,we employ statistical techniques to produce bias-corrected forecasts on a seasonal scale.This approach improves the accuracy of wind speed predictions at turbine hub height.Our assessment,based on root mean square error,reveals that bias-corrected wind speed forecasts show a significant improvement,ranging from 54%to 93%.展开更多
Unlike the traditional fossil energy, wind, as the clean renewable energy, can reduce the emission of the greenhouse gas. To take full advantage of the environmental benefits of wind energy, wind power forecasting has...Unlike the traditional fossil energy, wind, as the clean renewable energy, can reduce the emission of the greenhouse gas. To take full advantage of the environmental benefits of wind energy, wind power forecasting has to be studied to overcome the troubles brought by the variable nature of wind. Power forecasting for regional wind farm groups is the problem that many power system operators care about. The high-dimensional feature sets with redundant information are frequently encountered when dealing with this problem. In this paper, two kinds of feature set construction methods are proposed which can achieve the proper feature set either by selecting the subsets or by transforming the original variables with specific combinations. The former method selects the subset according to the criterion of minimal-redundancy-maximal-relevance (mRMR), while the latter does so based on the method of principal component analysis (PCA). A locally weighted learning method is also proposed to utilize the processed feature set to produce the power forecast results. The proposed model is simple and easy to use with parameters optimized automatically. Finally, a case study of 28 wind farms in East China is provided to verify the effectiveness of the proposed method.展开更多
The uncertainty of wind power forecasting significantly influences power systems with high percentage of wind power generation. Despite the wind power forecasting error causation, the temporal and spatial dependence o...The uncertainty of wind power forecasting significantly influences power systems with high percentage of wind power generation. Despite the wind power forecasting error causation, the temporal and spatial dependence of prediction errors has done great influence in specific applications, such as multistage scheduling and aggregated wind power integration. In this paper, Pair-Copula theory has been introduced to construct a multivariate model which can fully considers the margin distribution and stochastic dependence characteristics of wind power forecasting errors. The characteristics of temporal and spatial dependence have been modelled, and their influences on wind power integrations have been analyzed.Model comparisons indicate that the proposed model can reveal the essential relationships of wind power forecasting uncertainty, and describe the various dependences more accurately.展开更多
Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series an...Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.展开更多
Two-stage ensemble-based forecasting methods have been studied extensively in the wind power forecasting field. However, deep learning-based wind power forecasting studies have not investigated two aspects. In the fir...Two-stage ensemble-based forecasting methods have been studied extensively in the wind power forecasting field. However, deep learning-based wind power forecasting studies have not investigated two aspects. In the first stage, different learning structures considering multiple inputs and multiple outputs have not been discussed. In the second stage, the model extrapolation issue has not been investigated. Therefore, we develop four deep neural networks for the first stage to learn data features considering the input-and-output structure. We then explore the model extrapolation issue in the second stage using different modeling methods. Considering the overfitting issue, we propose a new moving window-based algorithm using a validation set in the first stage to update the training data in both stages with two different moving window processes. Experiments were conducted at three wind farms, and the results demonstrate that the model with single-input–multiple-output structure obtains better forecasting accuracy compared to existing models. In addition, the ridge regression method results in a better ensemble model that can further improve forecasting accuracy compared to existing machine learning methods. Finally, the proposed two-stage forecasting algorithm can generate more accurate and stable results than existing algorithms.展开更多
In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle ...In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.展开更多
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 w...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.展开更多
Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power generation.This study is intended to reduce the negative effects of such inaccurac...Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power generation.This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework(PDF)to choose useful data prior to modeling,thus improving the accuracy of day-ahead wind power forecasting.Briefly,we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model.Although a small subset can increase selection flexibility,it can also produce billions of subset combinations,resulting in computational issues.To address this problem,we incorporated metamodeling and optimization steps into PDF.We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm,respectively.Experimental results demonstrate that(1)it is necessary to select data before constructing a forecasting model;(2)using a smaller subset will likely increase selection flexibility,leading to a more accurate forecasting model;(3)PDF can generate a better training dataset than similarity-based data selection methods(e.g.,K-means and support vector classification);and(4)choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.展开更多
Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improvin...Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy.To improve forecasting accuracy,this paper focuses on two aspects:①proposing a novel hybrid method using Boosting algorithm and a multistep forecast approach to improve the forecasting capacity of traditional ARMA model;②calculating the existing error bounds of the proposed method.To validate the effectiveness of the novel hybrid method,one-year period of real data are used for test,which were collected from three operating wind farms in the east coast of Jiangsu Province,China.Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared.Test results show that the proposed method achieves a more accurate forecast.展开更多
In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power dispatch.Conventional univariate wind power forecasting ...In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power dispatch.Conventional univariate wind power forecasting methods at presentonly utilize individual wind farm historical data.However,studies have shown that forecasting accuracy canbe improved by exploring both spatial and temporal correlations among adjacent wind farms.Current research on spatial-temporal wind power forecasting is based on relatively shallow time series models that,to date,have demonstrated unsatisfactory performance.In this paper,a convolution operation is used to capture the spatial and temporal correlations among multiple wind farms.A novel convolution-based spatial-temporal wind power predictor(CSTWPP)is developed.Due to CSTWPP’s high nonlinearity and deep architecture,wind power variation features and regularities included in the historical data can be more effectively extracted.Furthermore,the online training of CSTWPP enables incremental learning,which makes CSTWPP non-stationary and in conformity with real scenarios.Graphics processing units(GPU)is used to speed up the training process,validating the developed CSTWPP for real-time application.Case studies on 28 adjacent wind farms are conducted to show that the proposed model can achieve superior performance on 5-30 minutes ahead wind power forecasts.展开更多
Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what specific forecasting algorithms are applied. The error correction model should be presented consi...Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what specific forecasting algorithms are applied. The error correction model should be presented considering not only the nonlinear and non-stationary characteristics of forecasting errors but also the field application adaptability problems. The kernel recursive least-squares(KRLS) model is introduced to meet the requirements of online error correction. An iterative error modification approach is designed in this paper to yield the potential benefits of statistical models, including a set of error forecasting models. The teleconnection in forecasting errors from aggregated wind farms serves as the physical background to choose the hybrid regression variables. A case study based on field data is found to validate the properties of the proposed approach. The results show that our approach could effectively extend the modifying horizon of statistical models and has a better performance than the traditional linear method for amending short-term forecasts.展开更多
Wind energy has been widely utilized to alleviate the shortage of fossil resources.When wind power is integrated into the power grid on a large scale,the power grid’s stability is severely harmed due to the fluctuati...Wind energy has been widely utilized to alleviate the shortage of fossil resources.When wind power is integrated into the power grid on a large scale,the power grid’s stability is severely harmed due to the fluctuating and intermittent properties of wind speed.Accurate wind power forecasts help to formulate good operational strategies for wind farms.A short-term wind power forecasting method based on new hybrid model is proposed to increase the accuracy of wind power forecast.Firstly,wind power time series are separated using the complete ensemble empirical mode decomposition with adaptive noise method to obtain multiple components,which are then predicted using a support vector regression machine model optimized through using the grid search and cross validation(GridSearchCV)algorithm.Secondly,a residual modification model based on temporal convolutional network is constructed,and variables with high correlation are selected as the input features of the model to predict the residuals of wind power.Finally,the prediction accuracy of the proposed method is compared to other models using the actual wind power data of the wind farm to demonstrate the validity of the described method,and the results reveal that the proposed method has better prediction performance.展开更多
基金the Deanship of Graduate Studies and Scientific Research at University of Bisha,Saudi Arabia for funding this research work through the Promising Program under Grant Number(UB-Promising-42-1445).
文摘Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output.This enhances the efficiency and reliability of renewable energy systems.Forecasting approaches inform energy management strategies,reduce reliance on fossil fuels,and support the broader transition to sustainable energy solutions.The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis.This research advances an optimized Multilayer Perceptron(MLP)model using recently proposedmetaheuristic optimization algorithms,namely the FireHawk Optimizer(FHO)and the Non-Monopolize Search(NO).A modified version of FHO,termed FHONO,is developed by integrating NO as a local search mechanism to enhance the exploration capability and address the shortcomings of the original FHO.The developed FHONO is then employed to optimize the MLP for enhanced wind power prediction.The effectiveness of the proposed FHONO-MLP model is validated using renowned datasets from wind turbines in France.The results of the comparative analysis between FHONO-MLP,conventionalMLP,and other optimized versions of MLP show that FHONO-MLP outperforms the others,achieving an average RootMean Square Error(RMSE)of 0.105,Mean Absolute Error(MAE)of 0.082,and Coefficient of Determination(R^(2))of 0.967 across all datasets.These findings underscore the significant enhancement in predictive accuracy provided by FHONO and demonstrate its effectiveness in improving wind power forecasting.
基金the National Natural Science Foundation of China(NSFC)(Nos.61806087,61902158)Jiangsu Province Natural Science Research Projects(No.17KJB470002)+1 种基金Natural science youth fund of Jiangsu province(No.BK20150471)Jiangsu University of Science and Technology Youth Science and Technology Polytechnic Innovation Project(No.1132931804)。
文摘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.
基金This research is supported by National Natural Science Foundation of China(No.61902158).
文摘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%.
文摘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.
文摘Wind power generation is among the most promising and eco-friendly energy sources today. Wind Power Forecasting (WPF) is essential for boosting energy efficiency and maintaining the operational stability of power grids. However, predicting wind power comes with significant challenges, such as weather uncertainties, wind variability, complex terrain, limited data, insufficient measurement infrastructure, intricate interdependencies, and short lead times. These factors make it difficult to accurately forecast wind behavior and respond to sudden power output changes. This study aims to precisely forecast electricity generation from wind turbines, minimize grid operation uncertainties, and enhance grid reliability. It leverages historical wind farm data and Numerical Weather Prediction data, using k-Nearest Neighbors for pre-processing, K-means clustering for categorization, and Long Short-Term Memory (LSTM) networks for training and testing, with model performance evaluated across multiple metrics. The Grey Wolf Optimized (GWO) LSTM classification technique, a deep learning model suited to time series analysis, effectively handles temporal dependencies in input data through memory cells and gradient-based optimization. Inspired by grey wolves’ hunting strategies, GWO is a population-based metaheuristic optimization algorithm known for its strong performance across diverse optimization tasks. The proposed Grey Wolf Optimized Deep Learning model achieves an R-squared value of 0.97279, demonstrating that it explains 97.28% of the variance in wind power data. This model surpasses a reference study that achieved an R-squared value of 0.92 with a hybrid deep learning approach but did not account for outliers or anomalous data.
基金supported by the National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption)under Grant(2018YFB0904200).
文摘Wind power forecasting(WPF)is important for safe,stable,and reliable integration of new energy technologies into power systems.Machine learning(ML)algorithms have recently attracted increasing attention in the field of WPF.However,opaque decisions and lack of trustworthiness of black-box models for WPF could cause scheduling risks.This study develops a method for identifying risky models in practical applications and avoiding the risks.First,a local interpretable model-agnostic explanations algorithm is introduced and improved for WPF model analysis.On that basis,a novel index is presented to quantify the level at which neural networks or other black-box models can trust features involved in training.Then,by revealing the operational mechanism for local samples,human interpretability of the black-box model is examined under different accuracies,time horizons,and seasons.This interpretability provides a basis for several technical routes for WPF from the viewpoint of the forecasting model.Moreover,further improvements in accuracy of WPF are explored by evaluating possibilities of using interpretable ML models that use multi-horizons global trust modeling and multi-seasons interpretable feature selection methods.Experimental results from a wind farm in China show that error can be robustly reduced.
基金supported in part by the National Natural Science Foundation of China(Grant/Award Numbers:U24B2083 and 52407098)the Fundamental Research Funds for the Central Uni-versities(Grant/Award Numbers:2025JC001 and 2024MS009).
文摘Wind power forecasting(WPF)accuracy is fundamentally constrained by two critical challenges.First,the high-order nonlinear relationship between wind speed(WS)and power(WP)substantially amplifies errors inherent in numerical weather prediction(NWP)data.Second,conventional models process all input features uniformly,failing to distinguish the dominant role of the primary driving feature from the complementary roles of auxiliary features.To decouple and address these challenges,this paper proposes a novel forecasting method(CFRM-DCM)that integrates a Correction Factor Representation Model(CFRM)and a Dual-Channel Mechanism(DCM).The CFRM is first employed to address the NWP error.It describes the complex correlation and forecasting error between measured WS and NWP WS as correction factors.A generative adversarial network(GAN)is then utilized to learn the distribution of these factors and output a corrected,more accurate WS.This corrected data is then fed into the DCM,a dual-branch architecture designed to enhance complex feature extraction,overcoming the limitations of traditional single-channel structures.The proposed method is validated on four wind farms.Simulation results demonstrate that the CFRM-DCM method achieves significant improvements in WPF accuracy,with error reductions ranging from 3.9%to 9.4%across ultra-short-term and short-term timescales.This enhanced WPF performance is directly attributed to the model’s ability to first improve WS accuracy,with gains of 8.8%,7.6%,8.3%,and 8.8%for the respective farms.
基金funded by the State Grid Science and Technology Project“Research on Key Technologies for Prediction and Early Warning of Large-Scale Offshore Wind Power Ramp Events Based on Meteorological Data Enhancement”(4000-202318098A-1-1-ZN).
文摘The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.
文摘Accurate predictions of wind power generation several months in advance are crucial for the effective operation and maintenance of wind farms and for facilitating efficient power purchase planning.This study evaluates the performance of the seasonal prediction system of the National Centre for Medium-Range Weather Forecasting in forecasting near-surface winds.An analysis of 23 years of hindcast data,from 1993 to 2015,indicates that the seasonal prediction system effectively captures the inter-annual variability of near-surface winds.Specifically,predictions initialized in May demonstrate notable accuracy,with a skill score of 0.78 in predicting the sign of wind speed anomalies aggregated across various wind farms during the high wind season(June to August).Additionally,we critically examine the peculiarity of a case study from 2020,when the Indian wind industry experienced low power generation.To enhance forecasting accuracy,we employ statistical techniques to produce bias-corrected forecasts on a seasonal scale.This approach improves the accuracy of wind speed predictions at turbine hub height.Our assessment,based on root mean square error,reveals that bias-corrected wind speed forecasts show a significant improvement,ranging from 54%to 93%.
文摘Unlike the traditional fossil energy, wind, as the clean renewable energy, can reduce the emission of the greenhouse gas. To take full advantage of the environmental benefits of wind energy, wind power forecasting has to be studied to overcome the troubles brought by the variable nature of wind. Power forecasting for regional wind farm groups is the problem that many power system operators care about. The high-dimensional feature sets with redundant information are frequently encountered when dealing with this problem. In this paper, two kinds of feature set construction methods are proposed which can achieve the proper feature set either by selecting the subsets or by transforming the original variables with specific combinations. The former method selects the subset according to the criterion of minimal-redundancy-maximal-relevance (mRMR), while the latter does so based on the method of principal component analysis (PCA). A locally weighted learning method is also proposed to utilize the processed feature set to produce the power forecast results. The proposed model is simple and easy to use with parameters optimized automatically. Finally, a case study of 28 wind farms in East China is provided to verify the effectiveness of the proposed method.
基金supported by China’s National High Technology Research and Development Program(No.2012AA050207)China’s National Nature Science Foundation(No.51190101)Science and Technology Projects of the State Grid Corporation of China(No.SGHN0000DKJS130022)
文摘The uncertainty of wind power forecasting significantly influences power systems with high percentage of wind power generation. Despite the wind power forecasting error causation, the temporal and spatial dependence of prediction errors has done great influence in specific applications, such as multistage scheduling and aggregated wind power integration. In this paper, Pair-Copula theory has been introduced to construct a multivariate model which can fully considers the margin distribution and stochastic dependence characteristics of wind power forecasting errors. The characteristics of temporal and spatial dependence have been modelled, and their influences on wind power integrations have been analyzed.Model comparisons indicate that the proposed model can reveal the essential relationships of wind power forecasting uncertainty, and describe the various dependences more accurately.
基金funded by National Natural Science Foundation of China (51577025).
文摘Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.
基金This study was supported by the Heilongjiang Provincial Postdoctoral Science Foundation(Grant No.LBH-Z19146)the China Postdoctoral Science Foundation(Grant No.216734)the National Natural Science Foundation of China(Grant No.91846301).
文摘Two-stage ensemble-based forecasting methods have been studied extensively in the wind power forecasting field. However, deep learning-based wind power forecasting studies have not investigated two aspects. In the first stage, different learning structures considering multiple inputs and multiple outputs have not been discussed. In the second stage, the model extrapolation issue has not been investigated. Therefore, we develop four deep neural networks for the first stage to learn data features considering the input-and-output structure. We then explore the model extrapolation issue in the second stage using different modeling methods. Considering the overfitting issue, we propose a new moving window-based algorithm using a validation set in the first stage to update the training data in both stages with two different moving window processes. Experiments were conducted at three wind farms, and the results demonstrate that the model with single-input–multiple-output structure obtains better forecasting accuracy compared to existing models. In addition, the ridge regression method results in a better ensemble model that can further improve forecasting accuracy compared to existing machine learning methods. Finally, the proposed two-stage forecasting algorithm can generate more accurate and stable results than existing algorithms.
文摘In this study,a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions.Using data on wind speed,air temperature,nacelle position,and actual power,lagged features were generated to capture temporal dependencies.Among 24 evaluated models,the ensemble bagging approach achieved the best performance,with R^(2) values of 0.89 at 0 min and 0.75 at 60 min.Shapley Additive exPlanations(SHAP)analysis revealed that while wind speed is the primary driver for short-term predictions,air temperature and nacelle position become more influential at longer forecasting horizons.These findings underscore the reliability of short-term predictions and the potential benefits of integrating hybrid AI and probabilistic models for extended forecasts.Our work contributes a robust and explainable framework to support Sri Lanka’s renewable energy transition,and future research will focus on real-time deployment and uncertainty quantification.
基金This project is supported by the National Natural Science Foundation of China(NSFC)(Nos.61806087,61902158).
文摘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.
基金supported by the National Natural Science Foundation of China(72101066,72131005,72121001,72171062,91846301,and 71772053)Heilongjiang Natural Science Excellent Youth Fund(YQ2022G004)Key Research and Development Projects of Heilongjiang Province(JD22A003).
文摘Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power generation.This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework(PDF)to choose useful data prior to modeling,thus improving the accuracy of day-ahead wind power forecasting.Briefly,we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model.Although a small subset can increase selection flexibility,it can also produce billions of subset combinations,resulting in computational issues.To address this problem,we incorporated metamodeling and optimization steps into PDF.We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm,respectively.Experimental results demonstrate that(1)it is necessary to select data before constructing a forecasting model;(2)using a smaller subset will likely increase selection flexibility,leading to a more accurate forecasting model;(3)PDF can generate a better training dataset than similarity-based data selection methods(e.g.,K-means and support vector classification);and(4)choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.
基金supported by the National High Technology Research and Development of China (863 Program) (No. 2012AA050214)the National Natural Science Foundation of China (No. 51077043)the State Grid Corporation of China (Impact research of source-grid-load interaction on operation and control of future power system)
文摘Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy.To improve forecasting accuracy,this paper focuses on two aspects:①proposing a novel hybrid method using Boosting algorithm and a multistep forecast approach to improve the forecasting capacity of traditional ARMA model;②calculating the existing error bounds of the proposed method.To validate the effectiveness of the novel hybrid method,one-year period of real data are used for test,which were collected from three operating wind farms in the east coast of Jiangsu Province,China.Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared.Test results show that the proposed method achieves a more accurate forecast.
文摘In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power dispatch.Conventional univariate wind power forecasting methods at presentonly utilize individual wind farm historical data.However,studies have shown that forecasting accuracy canbe improved by exploring both spatial and temporal correlations among adjacent wind farms.Current research on spatial-temporal wind power forecasting is based on relatively shallow time series models that,to date,have demonstrated unsatisfactory performance.In this paper,a convolution operation is used to capture the spatial and temporal correlations among multiple wind farms.A novel convolution-based spatial-temporal wind power predictor(CSTWPP)is developed.Due to CSTWPP’s high nonlinearity and deep architecture,wind power variation features and regularities included in the historical data can be more effectively extracted.Furthermore,the online training of CSTWPP enables incremental learning,which makes CSTWPP non-stationary and in conformity with real scenarios.Graphics processing units(GPU)is used to speed up the training process,validating the developed CSTWPP for real-time application.Case studies on 28 adjacent wind farms are conducted to show that the proposed model can achieve superior performance on 5-30 minutes ahead wind power forecasts.
基金partly supported by National Natural Science Foundation of China(No.51190101)science and technology project of State Grid,Research on the combined planning method for renewable power base based on multi-dimensional characteristics of wind and solar energy
文摘Forecasting error amending is a universal solution to improve short-term wind power forecasting accuracy no matter what specific forecasting algorithms are applied. The error correction model should be presented considering not only the nonlinear and non-stationary characteristics of forecasting errors but also the field application adaptability problems. The kernel recursive least-squares(KRLS) model is introduced to meet the requirements of online error correction. An iterative error modification approach is designed in this paper to yield the potential benefits of statistical models, including a set of error forecasting models. The teleconnection in forecasting errors from aggregated wind farms serves as the physical background to choose the hybrid regression variables. A case study based on field data is found to validate the properties of the proposed approach. The results show that our approach could effectively extend the modifying horizon of statistical models and has a better performance than the traditional linear method for amending short-term forecasts.
基金supported by National Defense Basic Research Program(JCKY2019407C002).
文摘Wind energy has been widely utilized to alleviate the shortage of fossil resources.When wind power is integrated into the power grid on a large scale,the power grid’s stability is severely harmed due to the fluctuating and intermittent properties of wind speed.Accurate wind power forecasts help to formulate good operational strategies for wind farms.A short-term wind power forecasting method based on new hybrid model is proposed to increase the accuracy of wind power forecast.Firstly,wind power time series are separated using the complete ensemble empirical mode decomposition with adaptive noise method to obtain multiple components,which are then predicted using a support vector regression machine model optimized through using the grid search and cross validation(GridSearchCV)algorithm.Secondly,a residual modification model based on temporal convolutional network is constructed,and variables with high correlation are selected as the input features of the model to predict the residuals of wind power.Finally,the prediction accuracy of the proposed method is compared to other models using the actual wind power data of the wind farm to demonstrate the validity of the described method,and the results reveal that the proposed method has better prediction performance.
