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.

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.

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%.

A novel pure data-selection framework for day-ahead wind power forecasting

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.

Regional wind power forecasting model with NWP grid dataoptimized

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.

Wind power forecasting errors modelling approach considering temporal and spatial dependence

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.

Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting

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.

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.

Two‐stage short‐term wind power forecasting algorithm using different feature-learning models

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.

Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

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.

Very Short-term Spatial and Temporal Wind Power Forecasting: A Deep Learning Approach

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.

Modelling of wind power forecasting errors based on kernel recursive least-squares method

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.

Wind power forecasting based on new hybrid model with TCN residual modification

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.

Spatio-temporal Convolutional Network Based Power Forecasting of Multiple Wind Farms

The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power forecasting of mul-tiple wind farms,determining the spatio-temporal correlation of multiple wind farms is critical for improving the forecasting accuracy.This paper proposes a spatio-temporal convolutional network(STCN)that utilizes a directed graph convolutional structure.A temporal convolutional network is also adopted to characterize the temporal features of wind power.Historical data from 15 wind farms in Australia are used in the case study.The forecasting results show that the proposed model has higher accuracy than the existing methods.Based on the structure of the STCN,asymmetric spatial correlation at different temporal scales can be observed,which shows the effectiveness of the proposed model.

Performance Improvement of Artificial Neural Network Model in Short-term Forecasting of Wind Farm Power Output

Due to the low dispatchability of wind power,the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible.A study is conducted in the present paper of potential improvements to the performance of artificial neural network(ANN)models in terms of efficiency and stability.Generally,current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station,in addition to selecting a fixed number of time periods prior to the forecasting.In this respect,new ANN models are proposed in this paper,which are developed by:varying the number of prior 1-h periods(periods prior to the forecasting hour)chosen for the input layer parameters;and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station.It has been found that the model performance is always improved when data from a second weather station are incorporated.The mean absolute relative error(MARE)of the new models is reduced by up to 7.5%.Furthermore,the longer the forecasting horizon,the greater the degree of improvement.

Short-term wind power prediction based on extreme learning machine with error correction

Introduction:Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind.The fluctuation of the wind generation has a great impact on the unit commitment.Thus accurate wind power forecasting plays a key role in dealing with the challenges of power system operation under uncertainties in an economical and technical way.Methods:In this paper,a combined approach based on Extreme Learning Machine(ELM)and an error correction model is proposed to predict wind power in the short-term time scale.Firstly an ELM is utilized to forecast the short-term wind power.Then the ultra-short-term wind power forecasting is acquired based on processing the short-term forecasting error by persistence method.Results:For short-term forecasting,the Extreme Learning Machine(ELM)doesn’t perform well.The overall NRMSE(Normalized Root Mean Square Error)of forecasting results for 66 days is 21.09%.For the ultra-short term forecasting after error correction,most of forecasting errors lie in the interval of[-10 MW,10 MW].The error distribution is concentrated and almost unbiased.The overall NRMSE is 5.76%.Conclusion:The ultra-short-term wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error(NRMSE).

Mixed Aleatory-epistemic Uncertainty Modeling of Wind Power Forecast Errors in Operation Reliability Evaluation of Power Systems

As the share of wind power in power systems continues to increase, the limited predictability of wind power generation brings serious potential risks to power system reliability. Previous research works have generally described the uncertainty of wind power forecast errors(WPFEs) based on normal distribution or other standard distribution models, which only characterize the aleatory uncertainty. In fact, epistemic uncertainty in WPFE modeling due to limited data and knowledge should also be addressed. This paper proposes a multi-source information fusion method(MSIFM) to quantify WPFEs when considering both aleatory and epistemic uncertainties. An extended focal element(EFE) selection method based on the adequacy of historical data is developed to consider the characteristics of WPFEs. Two supplementary expert information sources are modeled to improve the accuracy in the case of insufficient historical data. An operation reliability evaluation technique is also developed considering the proposed WPFE model. Finally,a double-layer Monte Carlo simulation method is introduced to generate a time-series output of the wind power. The effectiveness and accuracy of the proposed MSIFM are demonstrated through simulation results.

Multi-dimensional scenario forecast for generation of multiple wind farms

A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.

An introduction to multivariate probabilistic forecast evaluation

Probabilistic forecasting is becoming increasingly important for a wide range of applications,especially for en-ergy systems such as forecasting wind power production.A need for proper evaluation of probabilistic forecasts follows naturally with this,because evaluation is the key to improving the forecasts.Although plenty of excellent reviews and research papers on probabilistic forecast evaluation already exist,we find that there is a need for an introduction with some practical application.In particular,many forecast scenarios in energy systems are inher-ently multivariate,and while univariate evaluation methods are well understood and documented,only limited and scattered work has been done on their multivariate counterparts.This paper therefore contains a review of a selected set of probabilistic forecast evaluation methods,primarily scoring rules,as well as practical sections that explain how these methods can be calculated and estimated.In three case studies featuring simple autore-gressive models,stochastic differential equations and real wind power data,we implement,apply and discuss the logarithmic score,the continuous ranked probability score and the variogram score for forecasting problems of varying dimension.Finally,the advantages and disadvantages of the three scoring rules are highlighted,and this provides a significant step towards deciding on an evaluation method for a given multivariate forecast scenario including forecast scenarios relevant for energy systems.