A novel probabilistic short-term wind energy forecasting model based on an improved kernel density estimation

Guan, Jian; Lin, Jie; Guan, JianJie; Mokaramian, Elham

doi:10.1016/j.ijhydene.2020.06.209

Cited by 58 publications

(13 citation statements)

References 32 publications

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“…Recent papers addressing wind power forecasts could be broadly classified into 5 categories: papers focused on how to increase NWP accuracy [4][5][6][7][8], good-practice prediction guidelines [9][10][11], comparisons of accuracy across prediction models [12][13][14][15], hybrid and ensemble methods [16][17][18][19][20][21][22][23][24][25][26][27], and conventional methods improved by, among other things, preprocessing [28][29][30][31][32][33][34][35]. At this point, clear distinction should be made between hybrid, ensemble and improved models.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

et al. 2021

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The ability to forecast electricity generation for a small wind turbine is important both on a larger scale where there are many such turbines (because it creates problems for networks managed by distribution system operators) and for prosumers to allow current energy consumption planning. It is also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage. The research presented here addresses an original, rarely predicted 48 h forecasting horizon for small wind turbines. This topic has been rather underrepresented in research, especially in comparison with forecasts for large wind farms. Wind speed forecasts with a 48 h horizon are also rarely used as input data. We have analyzed the available data to identify potentially useful explanatory variables for forecasting models. Eight sets with increasing data amounts were created to analyze the influence of the types and amounts of data on forecast quality. Hybrid, ensemble and single methods are used for predictions, including machine learning (ML) solutions like long short-term memory (LSTM), multi-layer perceptron (MLP), support vector regression (SVR) and K-nearest neighbours regression (KNNR). Original hybrid methods, developed for research of specific implementations and ensemble methods based on hybrid methods’ decreased errors of energy generation forecasts for small wind turbines in comparison with single methods. The “artificial neural network (ANN) type MLP as an integrator of ensemble based on hybrid methods” ensemble forecasting method incorporates an original combination of predictors. Predictions by this method have the lowest mean absolute error (MAE). In addition, this paper presents an original ensemble forecasting method, called “averaging ensemble based on hybrid methods without extreme forecasts”. Predictions by this method have the lowest root mean square error (RMSE) error among all tested methods. LSTM, a deep neural network, is the best single method, MLP is the second best one, while SVR, KNNR and, especially, linear regression (LR) perform less well. We prove that lagged values of forecasted time series slightly increase the accuracy of predictions. The same applies to seasonal and daily variability markers. Our studies have also demonstrated that using the full set of available input data and the best proposed hybrid and ensemble methods yield the lowest error. The proposed hybrid and ensemble methods are also applicable to other short-time generation forecasting in renewable energy sources (RES), e.g., in photovoltaic (PV) systems or hydropower.

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Section: Related Workmentioning

confidence: 99%

“…Shadid et al, 2020 [33] propose preprocessing by wavelets, an approach popular in recent years, while Zhang et al, 2020 [34] provide modification to LSTM by constructing error following forget gate. Other contributions, focused largely on improvements of the existing methods-kernel density estimation (KDE) [35] and others.…”

Section: Related Workmentioning

confidence: 99%

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

et al. 2021

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“…The above methods have low computational complexity, but they are prone to meet situations with unreasonable distribution assumptions. In [10], the nonparametric kernel density estimation approach was used to obtain the distribution of prediction errors, which can avoid the impact of unreasonable assumptions of prior distribution, but the final probabilistic prediction results depend on the accuracy of point prediction, which often leads to poor generalization ability.…”

Section: Introductionmentioning

confidence: 99%

ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Niu

Zeng

et al. 2021

Energies

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Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified.

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“…Non-parametric model methods were also proposed in literature. These methods include the minimum cross entropy (MCE) method 41 , maximum entropy principle method (MEPM) 42 , kernel density estimation [43][44][45] and root-transformed local linear regression method 46 , etc. In this study, to evaluate wind energy potential, the single and mixture of two-parameter and three-parameter Weibull distributions were used as candidate models for wind speed data, and a finite mixture of voM distributions was used for wind direction data.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, to evaluate wind energy potential, the single and mixture of two-parameter and three-parameter Weibull distributions were used as candidate models for wind speed data, and a finite mixture of voM distributions was used for wind direction data. Based on MLE, the expectation-maximization (EM) 8,29,45,47 optimization algorithm is applied to estimate the model parameters of mixture distributions. As Carta et al 27 pointed out that although the mixture distributions enrich the modelling and have high degrees of fits, the model complexity increases with the increasing of more number of model parameters.…”

Section: Introductionmentioning

confidence: 99%

Wind energy potential assessment based on wind speed, its direction and power data

Wang

Liu

2021

Preprint

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Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters, based on expectation-maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as wind direction and relation circular variable models, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-minute field monitoring wind data and compared with the other estimation methods and judged by the values of R2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.

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A novel probabilistic short-term wind energy forecasting model based on an improved kernel density estimation

Cited by 58 publications

References 32 publications

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Wind energy potential assessment based on wind speed, its direction and power data

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