scholarly journals A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 254 ◽  
Author(s):  
Neeraj Bokde ◽  
Andrés Feijóo ◽  
Daniel Villanueva ◽  
Kishore Kulat
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Prediction Models ◽  
Wind Farms ◽  
Accurate Prediction ◽  
Intrinsic Mode Functions ◽  
Power Prediction ◽  
Planning And Scheduling ◽  
Mode Decomposition

Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.

A Method for Forecasting Short-Term Wind Speed Based on EMD and SVM

2013 ◽  
Vol 392 ◽  
pp. 622-627 ◽  
Author(s):  
Xiao Jing Dang ◽  
Hao Yong Chen ◽  
Xiao Ming Jin
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Power Prediction ◽  
Forecasting Accuracy ◽  
Mode Decomposition ◽  
Wind Power Prediction ◽  
Svm Model

In this paper, a method for wind speed forecasting based on Empirical Mode Decomposition and Support Vector machine is proposed. Compared with the approach based on Support Vector machine only, the method in this paper use EMD to decompose the data of wind power into several independent intrinsic mode functions (IMF),then model each component with the SVM model and get the final value of the overall wind power prediction. Experiments show the efficiency of the approach with a higher forecasting accuracy.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

scholarly journals Development of Wind Power Prediction Models for Pawan Danavi Wind Farm in Sri Lanka

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Piyal Ekanayake ◽  
Amila T. Peiris ◽  
J. M. Jeevani W. Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Wind Speed ◽  
Sri Lanka ◽  
Ambient Temperature ◽  
Wind Power ◽  
Correlation Coefficient ◽  
Wind Farm ◽  
Prediction Models ◽  
Power Prediction ◽  
Independent Variables ◽  
Wind Power Prediction

This paper presents the development of wind power prediction models for a wind farm in Sri Lanka using an artificial neural network (ANN), multiple linear regression (MLR), and power regression (PR) techniques. Power generation data over five years since 2015 were used as the dependent variable in modeling, while the corresponding wind speed and ambient temperature values were used as independent variables. Variation of these three variables over time was analyzed to identify monthly, seasonal, and annual patterns. The monthly patterns are coherent with the seasonal monsoon winds exhibiting little annual variation, in the absence of extreme meteorological changes during the period of 2015–2020. The correlation within each pair of variables was also examined by applying statistical techniques, which are presented in terms of Pearson’s and Spearman’s correlation coefficients. The impact of unit increase (or decrease) in the wind speed and ambient temperature around their mean values on the output power was also quantified. Finally, the accuracy of each model was evaluated by means of the correlation coefficient, root mean squared error (RMSE), bias, and the Nash number. All the models demonstrated acceptable accuracy with correlation coefficient and Nash number closer to 1, very low RMSE, and bias closer to 0. Although the ANN-based model is the most accurate due to advanced features in machine learning, it does not express the generated power output in terms of the independent variables. In contrast, the regression-based statistical models of MLR and PR are advantageous, providing an insight into modeling the power generated by the other wind farms in the same region, which are influenced by similar climate conditions.

Research on Wind Power Prediction Based on Distributed Wind Farms Siting

2013 ◽  
Vol 805-806 ◽  
pp. 312-315 ◽  
Author(s):  
Jun Yang ◽  
Zhao Qiang Zeng ◽  
Xu Huang ◽  
Qiu Ye Sun
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farms ◽  
New Method ◽  
Prediction System ◽  
Power Prediction ◽  
Roughness Model ◽  
Wind Power Prediction

This paper proposes a new method to predict the wind power of the distributed wind farms, considering the models such as the roughness model, the orography model. In order to predict the wind power accurately, this method calculates the loss of the wind speed directly, caused by the roughness model and the orography model. At the same time, this paper proposed the structure of the wind power prediction system, which provides the reference for the prediction of the wind power of distributed wind farms.

scholarly journals Application of Ensemble Empirical Mode Decomposition based Support Vector Regression Model for Wind Power Prediction

2020 ◽  
Vol 22 (1) ◽  
pp. 11-16
Author(s):  
Irene Karijadi ◽  
Ig. Jaka Mulyana
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
System Stability ◽  
Support Vector ◽  
Power Prediction ◽  
Mode Decomposition ◽  
Wind Power Prediction

Improving accuracy of wind power prediction is important to maintain power system stability. However, wind power prediction is difficult due to randomness and high volatility characteristics. This study applies a hybrid algorithm that combines ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to develop a prediction model for wind power prediction. Ensemble empirical mode decomposition is employed to decompose original data into several Intrinsic Mode Functions (IMF). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of wind power Numerical testing demonstrated that the proposed method can accurately predict the wind power in Belgian.

scholarly journals The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1666 ◽  
Author(s):  
Neeraj Bokde ◽  
Andrés Feijóo ◽  
Nadhir Al-Ansari ◽  
Siyu Tao ◽  
Zaher Mundher Yaseen
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Arima Model ◽  
Ensemble Empirical Mode Decomposition ◽  
Power Modeling ◽  
Short Term ◽  
Mode Decomposition ◽  
Eemd Method

In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.

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