A Research on Wind Power Forecasting Techniques

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.

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Study of Short-Term Wind Power Forecasting Based on Adaptive Grey Prediction Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.734.697 ◽

2015 ◽

Vol 734 ◽

pp. 697-700 ◽

Cited By ~ 1

Author(s):

Wen Zhen Cai ◽

Dong Tao Wang ◽

Yuan Song Wang ◽

Yong Yang ◽

Zhi Long Gao

Keyword(s):

Wind Power ◽

Large Scale ◽

Prediction Method ◽

Test System ◽

Grey Prediction ◽

Grey System ◽

Short Term ◽

Wind Power Forecasting ◽

Short Term Forecasting ◽

Power Forecasting

With the wind power developing fast in the world, the large scale of wind power integration in power system leads to great challenges, and the wind power forecasting will play a key role in dealing with these challenges. A wind power short-term forecasting method based on grey system is introduced in this paper. Firstly, a basic model of grey prediction method is given. Then, in order to smoothen the basic data for the grey modeling, a self adaptive grey prediction method is developed. Finally, the result of prediction for a test system of wind power are presented and the effectiveness of the method given by the paper has been proved.

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Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.875-877.1858 ◽

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.

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Study of Wind Power Forecasting Assessment Standard Based on Unit Commitment and Spinning Reserve Optimization Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.608-609.547 ◽

2012 ◽

Vol 608-609 ◽

pp. 547-552

Author(s):

Chun Jie Gao ◽

Peng Wang

Keyword(s):

Wind Power ◽

Power Output ◽

Large Scale ◽

North China ◽

Wind Farms ◽

Spinning Reserve ◽

Wind Power Forecasting ◽

Assessment Standard ◽

North China Area ◽

Power Forecasting

After large-scale wind power integrate into the system, there is a great impact for the system dispatching operation and the unit maintenance and repair of the wind power , so it's extremely necessary to forecast wind power output and assess its level of forecasting. This paper mainly focusing on the containing wind power system, studies the wind power output fluctuation in the demand for system reserve, and analyse the rationality of the wind power forecasting assessment standard in North China area wind power integration operation management implementing regulations by combining with the status of wind power in North China area, that is, whether the assessment mechanism can promote wind farms raising the forecasting level.

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

Energies ◽

10.3390/en14206500 ◽

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.

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Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.483.275 ◽

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.

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

Advances in Mathematical Physics ◽

10.1155/2014/637017 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 2

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.

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Area automatic voltage control based on wind power forecasting of large-scale wind farms

IEEE PES Innovative Smart Grid Technologies ◽

10.1109/isgt-asia.2012.6303197 ◽

2012 ◽

Cited By ~ 3

Author(s):

Xuefang Liu ◽

Hongtao Wang

Keyword(s):

Wind Power ◽

Large Scale ◽

Voltage Control ◽

Wind Farms ◽

Wind Power Forecasting ◽

Power Forecasting

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A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs

Applied Sciences ◽

10.3390/app11031100 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1100

Author(s):

Quoc Thang Phan ◽

Yuan Kang Wu ◽

Quoc Dung Phan

Keyword(s):

Deep Learning ◽

Wind Power ◽

Weather Prediction ◽

Data Preprocessing ◽

Forecasting Model ◽

Efficient Operation ◽

Wind Power Forecasting ◽

Wind Speeds ◽

Probabilistic Forecasts ◽

Power Forecasting

In recent years, wind energy has become a competitively priced source of energy around the world, which has created increasing challenges for system operators. Accurate wind power generation forecasting plays an important role in power systems to improve the reliable and efficient operation. Therefore, numerous artificial intelligent methods such as machine learning and deep learning have been considered as solutions for accurate wind power forecasts. In addition to deterministic forecasting, the probabilistic forecasting becomes more important, because it indicates the level of uncertainty. In this paper, a hybrid forecasting model considering different Numerical Weather Prediction (NWP) models and the XGBoost training model is proposed for short-term wind power forecasting. The proposed forecasting algorithm includes data preprocessing, in which an autoencoder model is used to reduce the dimension of 20 NWP ensembles. The performance of the proposed method is investigated using historical wind power measurements and NWP results by the Taiwan Central Weather Bureau (CWB); the NWP includes spot wind speeds from WRFD, RWRF, and ensemble wind speeds from WEPS. Based on the forecasting results, the proposed model produces better performance and forecasting accuracy among other forecasting models, which reveals the importance of data preprocessing using autoencoders and the use of deep learning models in deterministic or probabilistic forecasts.

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