A study on comparing short-term wind power prediction models in Gunsan wind farm

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.

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M2GSNet: Multi-Modal Multi-Task Graph Spatiotemporal Network for Ultra-Short-Term Wind Farm Cluster Power Prediction

Applied Sciences ◽

10.3390/app10217915 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7915

Author(s):

Hang Fan ◽

Xuemin Zhang ◽

Shengwei Mei ◽

Kunjin Chen ◽

Xinyang Chen

Keyword(s):

Wind Power ◽

Large Scale ◽

Wind Farm ◽

Weather Prediction ◽

Wind Farms ◽

Task Graph ◽

Probabilistic Prediction ◽

Power Prediction ◽

Short Term ◽

Wind Power Prediction

Ultra-short-term wind power prediction is of great importance for the integration of renewable energy. It is the foundation of probabilistic prediction and even a slight increase in the prediction accuracy can exert significant improvement for the safe and economic operation of power systems. However, due to the complex spatiotemporal relationship and the intrinsic characteristic of nonlinear, randomness and intermittence, the prediction of regional wind farm clusters and each wind farm’s power is still a challenge. In this paper, a framework based on graph neural network and numerical weather prediction (NWP) is proposed for the ultra-short-term wind power prediction. First, the adjacent matrix of wind farms, which are regarded as the vertexes of a graph, is defined based on geographical distance. Second, two graph neural networks are designed to extract the spatiotemporal feature of historical wind power and NWP information separately. Then, these features are fused based on multi-modal learning. Third, to enhance the efficiency of prediction method, a multi-task learning method is adopted to extract the common feature of the regional wind farm cluster and it can output the prediction of each wind farm at the same time. The cases of a wind farm cluster located in Northeast China verified that the accuracy of a regional wind farm cluster power prediction is improved, and the time consumption increases slowly when the number of wind farms grows. The results indicate that this method has great potential to be used in large-scale wind farm clusters.

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Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Sustainability ◽

10.3390/su11030650 ◽

2019 ◽

Vol 11 (3) ◽

pp. 650 ◽

Cited By ~ 6

Author(s):

Jianguo Zhou ◽

Xiaolei Xu ◽

Xuejing Huo ◽

Yushuo Li

Keyword(s):

Wind Speed ◽

Wind Power ◽

Wind Farm ◽

Meteorological Data ◽

Frequency Component ◽

High Frequency Component ◽

Power Prediction ◽

Short Term ◽

Combined Model ◽

Wind Power Prediction

The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models.

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Ultra-Short Term Wind Power Prediction Model Based on Modified Grey Model Method for Power Control in Wind Farm

Wind Engineering ◽

10.1260/0309-524x.35.1.55 ◽

2011 ◽

Vol 35 (1) ◽

pp. 55-67 ◽

Cited By ~ 16

Author(s):

Zhen. Zhen Zhang ◽

Jian. Xiao Zou ◽

Gang Zheng

Keyword(s):

Prediction Model ◽

Power Control ◽

Wind Power ◽

Wind Farm ◽

Grey Model ◽

Power Prediction ◽

Short Term ◽

Model Method ◽

Model Based ◽

Wind Power Prediction

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Short-Term Wind Power Forecasting Based on SVM with Backstepping Wind Speed of Power Curve

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.224.401 ◽

2012 ◽

Vol 224 ◽

pp. 401-405

Author(s):

Xi Yun Yang ◽

Peng Wei ◽

Huan Liu ◽

Bao Jun Sun

Keyword(s):

Wind Speed ◽

Wind Power ◽

Power Plants ◽

Wind Farm ◽

Prediction Method ◽

Power Curve ◽

Power Prediction ◽

Short Term ◽

Wind Power Forecasting ◽

Wind Power Prediction

Accurate wind farm power prediction can relieve the disadvantageous impact of wind power plants on power systems and reduce the difficulty of the scheduling of power dispatching department. Improving accuracy of short-term wind speed prediction is the key of wind power prediction. The authors have studied the short-term wind power forecasting of power plants and proposed a model prediction method based on SVM with backstepping wind speed of power curve. In this method, the sequence of wind speed that is calculated according to the average power of the wind farm operating units and the scene of the power curve is the input of the SVM model. The results show that this method can meet the real-time needs of the prediction system, but also has better prediction accuracy, is a very valuable short-term wind power prediction method.

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Error analysis of short term wind power prediction models

Applied Energy ◽

10.1016/j.apenergy.2010.10.035 ◽

2011 ◽

Vol 88 (4) ◽

pp. 1298-1311 ◽

Cited By ~ 99

Author(s):

Maria Grazia De Giorgi ◽

Antonio Ficarella ◽

Marco Tarantino

Keyword(s):

Error Analysis ◽

Wind Power ◽

Prediction Models ◽

Power Prediction ◽

Short Term ◽

Wind Power Prediction

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CMAC Applied in Wind Power Prediction Based on Applied Mechanics

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.496.75 ◽

2012 ◽

Vol 496 ◽

pp. 75-78

Author(s):

Yi Chuan Shao ◽

Xing Jia Yao

Keyword(s):

Wind Power ◽

Wind Farm ◽

Training Data ◽

Cerebellar Model Articulation Controller ◽

Applied Mechanics ◽

Power Prediction ◽

Short Term ◽

Term Prediction ◽

Wind Power Prediction ◽

Short Term Prediction

Wind Power prediction is very important in the wind power grid management. This paper introduces how to use Cerebellar Model Articulation Controller(CMAC) to build a short-term wind power prediction model.CMAC and Back-propagation Artificial Neural Networks(BP) are used respectively to do the short-term prediction with the data from a wind farm in Inner Mongolia. After comparison of the results, CMAC is more stable, accurate and faster with less training data.. CMAC is considered to be more suitable to do the short-term prediction. All of the study are based on applied mechanics, which will be useful for energy engineering and mechanics study.

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Ultra-Short-term Power Prediction of the Wind Farm Based on Multivariate Data Combination

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/898/1/012001 ◽

2021 ◽

Vol 898 (1) ◽

pp. 012001

Author(s):

Yong Jian ◽

Zhong Li ◽

Biao Li ◽

Xuyuan Cao ◽

Jiayuan Zhu

Keyword(s):

Wind Power ◽

Large Scale ◽

Wind Farm ◽

Measurement Data ◽

Power Prediction ◽

Short Term ◽

Actual Measurement ◽

Grid Connection ◽

Wind Power Prediction ◽

Weighted Values

Abstract Accurate wind power prediction is an important way to promote large-scale wind power grid connection. First, to address the abnormal wind farm actual measurement data caused by wind abandonment and power limitation, the DBSCAN method is used to pre-process the wind farm actual measurement data and eliminate the abnormal data. Then, a short-term wind power prediction model with a combination of GA-LSSVM and ARIMA weights is established, and the Lagrange multiplier algorithm is used to obtain the weighted values of each single model in the combined model to further obtain the wind power prediction results. Finally, the effectiveness of the proposed method is verified by arithmetic examples, and the results show that the proposed model and method can effectively improve the prediction accuracy of short-term wind power.

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

Wind Engineering ◽

10.1260/030952405776234599 ◽

2005 ◽

Vol 29 (6) ◽

pp. 475-489 ◽

Cited By ~ 161

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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Study on Short-Term Wind Power Prediction Model Based on ARMA Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.448-453.1875 ◽

2013 ◽

Vol 448-453 ◽

pp. 1875-1878 ◽

Cited By ~ 4

Author(s):

Wei Li ◽

Hong Tu Zhang ◽

Ting Ting An

Keyword(s):

Wind Power ◽

Wind Farm ◽

Arma Model ◽

Power Prediction ◽

Short Term ◽

Wind Power Integration ◽

Proposed Model ◽

Wind Power Prediction ◽

High Prediction ◽

New Research

At present, the difficulty of wind power integration has resulted in a large number of wind curtailment phenomena and wasted a lot of renewable energy. Due to the significant instability, anti-peak-regulation and intermittency of wind power, wind power integration needs an accurate prediction technique to be a basis. ARMA model has the advantage of high prediction accuracy in predicting short-term wind power. This paper puts forward the method for short-term wind power prediction using ARMA model and carries out empirical analysis using the data from a wind farm of Jilin province, which shows the science and operability of the proposed model. It provides a new research method for the wind power prediction.

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