Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method

2014 ◽  
Vol 875-877 ◽  
pp. 1858-1862
Author(s):  
Yun Yu ◽  
Bo Yang ◽  
Fu Jiang Ge
Keyword(s):  
Wind Power ◽  
Power Flow ◽  
Large Scale ◽  
Wind Farm ◽  
Utilization Rate ◽  
Wind Power Forecasting ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Grid Side ◽  
Power Forecasting

Accurate regional wind power forecasting guarantees the security and economics of the power system integrated with large scale of wind power. Aiming at the gross wind power output of the whole regional grid area, existing regional wind power forecasting methods fails to characterize the locally gross output power of the wind farm aggregation forming a power flow interface with specified flow restraints. In this paper, the work flow of the power flow oriented regional wind power forecasting method based on whole-grid regional wind power forecasting methods was presented first. Then, the data preparation, data preprocessing and the mathematical description of the algorithm for our method were presented. Finally, the case study proved the feasibility and effectiveness of our method. The conclusion indicates that the method presented in this paper implements a multiple temporal and spatial scale regional win power forecasting technology, which can obviously improve the accuracy of regional wind power forecasting, relieve the pressure for the grid side and improve the utilization rate of wind power.

Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method

2013 ◽  
Vol 483 ◽  
pp. 275-279
Author(s):  
Yun Yu ◽  
Bo Yang ◽  
Fu Jiang Ge
Keyword(s):  
Wind Power ◽  
Power Flow ◽  
Large Scale ◽  
Wind Farm ◽  
Utilization Rate ◽  
Wind Power Forecasting ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Grid Side ◽  
Power Forecasting

Accurate regional wind power forecasting guarantees the security and economics of the power system integrated with large scale of wind power. Aiming at the gross wind power output of the whole regional grid area, existing regional wind power forecasting methods fails to characterize the locally gross output power of the wind farm aggregation forming a power flow interface with specified flow restraints. In this paper, the work flow of the power flow oriented regional wind power forecasting method based on whole-grid regional wind power forecasting methods was presented first. Then, the data preparation, data preprocessing and the mathematical description of the algorithm for our method were presented. Finally, the case study proved the feasibility and effectiveness of our method. The conclusion indicates that the method presented in this paper implements a multiple temporal and spatial scale regional win power forecasting technology, which can obviously improve the accuracy of regional wind power forecasting, relieve the pressure for the grid side and improve the utilization rate of wind power.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

scholarly journals Short-term Power Forecasting Method and Application of Wind Farm

2018 ◽  
Vol 232 ◽  
pp. 04001
Author(s):  
Xiaohu Yang ◽  
Rong Ju ◽  
Zhe Yuan ◽  
Zhenya Zhang
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Normal Operation ◽  
Prediction Methods ◽  
Practical Application ◽  
Short Term ◽  
Forecasting Method ◽  
Wind Power System ◽  
Power Forecasting

The prediction of output power of wind farm has important value and significance to the normal operation of some large-scale wind power system. In this paper, the related prediction methods and practical application are studied, and the short-term power forecasting method of the wind power of the vector machine-Markov chain is proposed.

scholarly journals A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6500
Author(s):  
Jie Liu ◽  
Quan Shi ◽  
Ruilian Han ◽  
Juan Yang
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Pso Algorithm ◽  
Stable Operation ◽  
Feature Maps ◽  
Wind Power Forecasting ◽  
Factors Affecting ◽  
Using Data ◽  
Power Forecasting

Accurate and timely wind power forecasting is essential for achieving large-scale wind power grid integration and ensuring the safe and stable operation of the power system. For overcoming the inaccuracy of wind power forecasting caused by randomness and volatility, this study proposes a hybrid convolutional neural network (CNN) model (GA–PSO–CNN) integrating genetic algorithm (GA) and a particle swarm optimization (PSO). The model can establish feature maps between factors affecting wind power such as wind speed, wind direction, and temperature. Moreover, a mix-encoding GA–PSO algorithm is introduced to optimize the network hyperparameters and weights collaboratively, which solves the problem of subjective determination of the optimal network in the CNN and effectively prevents local optimization in the training process. The prediction effectiveness of the proposed model is verified using data from a wind farm in Ningxia, China. The results show that the MAE, MSE, and MAPE of the proposed GA–PSO–CNN model decreased by 1.13–9.55%, 0.46–7.98%, and 3.28–19.29%, respectively, in different seasons, compared with Single–CNN, PSO–CNN, ISSO–CNN, and CHACNN models. The convolution kernel size and number in each convolution layer were reduced by 5–18.4% in the GA–PSO–CNN model.

scholarly journals Developing a Local Neurofuzzy Model for Short-Term Wind Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
E. Faghihnia ◽  
S. Salahshour ◽  
A. Ahmadian ◽  
N. Senu
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Learning Algorithm ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Power Forecasting

Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations

2019 ◽  
Vol 135 ◽  
pp. 674-686 ◽  
Author(s):  
Miguel A. Prósper ◽  
Carlos Otero-Casal ◽  
Felipe Canoura Fernández ◽  
Gonzalo Miguez-Macho
Keyword(s):  
High Resolution ◽  
Wind Power ◽  
Complex Terrain ◽  
Wind Farm ◽  
Onshore Wind ◽  
Wind Power Forecasting ◽  
Power Forecasting

A Wind Power Forecasting Method and Its Confidence Interval Estimation

2013 ◽  
Vol 186 (2) ◽  
pp. 52-60 ◽  
Author(s):  
Tatsuya Iizaka ◽  
Toru Jintsugawa ◽  
Hideyuki Kondo ◽  
Yosuke Nakanishi ◽  
Yoshikazu Fukuyama ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Wind Power ◽  
Interval Estimation ◽  
Wind Power Forecasting ◽  
Forecasting Method ◽  
Confidence Interval Estimation ◽  
Power Forecasting
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