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.

Forecast of Short-Term Wind Power Based on GA Optimized Elman Neural Network

2014 ◽  
Vol 536-537 ◽  
pp. 470-475
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Prediction ◽  
Elman Neural Network ◽  
Wind Power Prediction ◽  
Forecasting Method ◽  
Safety And Stability

Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms with the power grid will bring about impact on the safety and stability of power systems. Based on the real-time wind power data, wind power prediction model using Elman neural network is proposed. At the same time in order to overcome the disadvantages of the Elman neural network for easily fall into local minimum and slow convergence speed, this paper put forward using the GA algorithm to optimize the weight and threshold of Elman neural network. Through the analysis of the measured data of one wind farm, shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

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

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.

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

2019 ◽  
Vol 11 (3) ◽  
pp. 650 ◽  
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.

scholarly journals Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 157 ◽  
Author(s):  
Pei Zhang ◽  
Yanling Wang ◽  
Likai Liang ◽  
Xing Li ◽  
Qingtian Duan
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Linear Regression Method ◽  
Power Prediction ◽  
Short Term ◽  
Outlier Identification ◽  
Dbscan Algorithm ◽  
Wind Power Prediction

Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic algorithm (GA-BP neural network). Analysis of the results proves the practicability and efficiency of the prediction model and the important role of outlier identification and correction in improving the accuracy of wind power prediction.

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