scholarly journals A Novel Multi-Step Cross-Decomposition Method Based on Wavelet Transform for Wind Power Prediction

2021 ◽  
Vol 252 ◽  
pp. 01015
Author(s):  
Jianxun Lang
Keyword(s):  
Wavelet Transform ◽  
Wind Speed ◽  
Prediction Model ◽  
Decomposition Method ◽  
Prediction Models ◽  
Wind Farms ◽  
Decomposition Methods ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Cross Decomposition

One of the main approaches to improve wind power prediction accuracy is to decompose wind-speed into different frequency-band components and use them as inputs of prediction model. Among the decomposition methods, wavelet transform is widely used due to its flexibility. However, the decomposition level and wavelet function need to be selected through trail-and-error, which is also called empirical decomposition method, because the effectiveness of a certain selection depends on the characteristic of wind farm and the prediction model. Therefore, it is difficult to find a general decomposition method that can be effective on different prediction models and wind farms. Aiming at this problem, a novel multi-step cross-decomposition method is proposed in this paper. The proposed method decomposes the wind-speed and power alternatively in each step, and after three steps of decomposition, the wind-speed can be decomposed to four different frequency-band components which will be used as the input of the prediction model. The prediction errors of proposed method and several empirical decomposition methods are compared on BPNN and SVM models. The results show that the proposed method is the only effective method on two prediction models for four wind farms.

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.

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.

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 A Multi-Step Prediction Method for Wind Power Based on Improved TCN to Correct Cumulative Error

2021 ◽  
Vol 9 ◽  
Author(s):  
Haifeng Luo ◽  
Xun Dou ◽  
Rong Sun ◽  
Shengjun Wu
Keyword(s):  
Power Systems ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Method ◽  
Input Sequence ◽  
Wind Farms ◽  
Power Prediction ◽  
Cumulative Error ◽  
Wind Power Prediction ◽  
Accuracy And Stability

Wind power generation is likely to hinder the safe and stable operations of power systems for its irregularity, intermittency, and non-smoothness. Since wind power is continuously connected to power systems, the step length required for predicting wind power is increasingly extended, thereby causing an increasing cumulative error. Correcting the cumulative error to predict wind power in multi-step is an urgent problem that needs to be solved. In this study, a multi-step wind power prediction method was proposed by exploiting improved TCN to correct the cumulative error. First, multi-scale convolution (MSC) and self-attentiveness (SA) were adopted to optimize the problem that a single-scale convolution kernel of TCN is difficult to extract temporal and spatial features at different scales of the input sequence. The MSC-SA-TCN model was built to recognize and extract different features exhibited by the input sequence to improve the accuracy and stability of the single-step prediction of wind power. On that basis, the multi-channel time convolutional network with multiple input and multiple output codec technologies was adopted to build the nonlinear mapping between the output and input of the TCN multi-step prediction. The method improved the problem that a single TCN is difficult to tap the different nonlinear relationships between the multi-step prediction output and the fixed input. The MMED-TCN multi-step wind power prediction model was developed to separate linearity and nonlinearity between input and output to reduce the multi-step prediction error. An experimental comparative analysis was conducted based on the measured data from two wind farms in Shuangzitai, Liaoning, and Keqi, Inner Mongolia. As revealed from the results, the MAE and RMSE of the MMED-TCN-based multi-step prediction model achieved the cumulative mean values of 0.0737 and 0.1018. The MAE and RMSE metrics outperformed those of the VMD-AMS-TCN and MSC-SA-TCN models. It can be seen that the wind power prediction method proposed in this study could improve the feature extraction ability of TCN for input sequences and the ability of mining the mapping relationship between multiple inputs and multiple outputs. The method is superior in terms of the accuracy and stability of wind power prediction.

Wind Power Prediction Model Based on ARMA and Improved BP-ANN

2014 ◽  
Vol 1008-1009 ◽  
pp. 183-187
Author(s):  
Hai Ke Liu ◽  
Jiang Xia Feng ◽  
Shen Quan Yang ◽  
Tao Jia
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Prediction Model ◽  
Wind Power ◽  
Absolute Error ◽  
Power Prediction ◽  
Forecasting Accuracy ◽  
Wind Power Prediction ◽  
Time Series Method ◽  
The Mean

In order to improve the prediction accuracy of wind power, this research is based on time series and improved BP-ANN algorithm. The basic idea is described as follows: wind speed forecasting model is established by using time series method; wind speed-wind power model is built by utilising improved BP-ANN algorithm; wind speed data from time series forecasting is used as input of neural network model, and the prediction results for wind power are obtained. In order to analyse the availability of wind power prediction model, the mean absolute error and correlation coefficient are compared to analyse the predictions results. The results show that the prediction model can effectively improve the forecasting accuracy of wind power.

scholarly journals Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models

2005 ◽  
Vol 29 (6) ◽  
pp. 475-489 ◽  
Author(s):  
Henrik Madsen ◽  
Pierre Pinson ◽  
George Kariniotakis ◽  
Henrik Aa. Nielsen ◽  
Torben S. Nielsen
Keyword(s):  
Wind Power ◽  
Electricity Markets ◽  
Large Scale ◽  
Prediction Models ◽  
Wind Farms ◽  
Offshore Wind ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction ◽  
Prediction Systems

Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term windpower prediction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated, using results from both on-shore and offshore wind farms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems.

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