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

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.

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

Energies ◽

10.3390/en12183586 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3586 ◽

Cited By ~ 1

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.

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Study on a Service Mechanism that Wind/Photovoltaic/Energy Storage Power Station Help Wind Farms Tracking Generation Schedule Curve

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.860-863.1909 ◽

2013 ◽

Vol 860-863 ◽

pp. 1909-1913

Author(s):

Hai Xiang Xu ◽

Peng Wang ◽

Xiao Meng Ren

Keyword(s):

Energy Storage ◽

Wind Power ◽

Wind Farm ◽

Wind Farms ◽

Power Station ◽

Forecasting Accuracy ◽

Wind Power Forecasting ◽

Assessment Standard ◽

The Impact ◽

Power Forecasting

At present, the technology of wind power forecasting isn‘t mature enough in china, so some grid-connected wind farms will be assessed when theirs power forecasting accuracy cant reach the assessment standard. In response to the situation, combined with the characteristics of WPSPS and wind farms, this paper designs a service mechanism that WPSPS help wind farms tracking generation schedule curve, namely, encouraging WPSPS to supply output compensation service for wind farm by market means to increase the accuracy of wind power forecasting. By this mechanism, not only WPSPS and wind farms will achieve win-win, but also the impact on the grid caused by fluctuations of wind powers output will reduce.

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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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Integrated Machine Learning and Enhanced Statistical Approach-Based Wind Power Forecasting in Australian Tasmania Wind Farm

Complexity ◽

10.1155/2020/9250937 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Fang Yao ◽

Wei Liu ◽

Xingyong Zhao ◽

Li Song

Keyword(s):

Machine Learning ◽

Wind Speed ◽

Wind Power ◽

Wind Farm ◽

Statistical Approach ◽

Short Term Memory ◽

Prediction Interval ◽

Wind Power Forecasting ◽

Proposed Model ◽

Power Forecasting

This paper develops an integrated machine learning and enhanced statistical approach for wind power interval forecasting. A time-series wind power forecasting model is formulated as the theoretical basis of our method. The proposed model takes into account two important characteristics of wind speed: the nonlinearity and the time-changing distribution. Based on the proposed model, six machine learning regression algorithms are employed to forecast the prediction interval of the wind power output. The six methods are tested using real wind speed data collected at a wind station in Australia. For wind speed forecasting, the long short-term memory (LSTM) network algorithm outperforms other five algorithms. In terms of the prediction interval, the five nonlinear algorithms show superior performances. The case studies demonstrate that combined with an appropriate nonlinear machine learning regression algorithm, the proposed methodology is effective in wind power interval forecasting.

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A wind power forecasting system based on the weather research and forecasting model and Kalman filtering over a wind-farm in Japan

Journal of Renewable and Sustainable Energy ◽

10.1063/1.4940208 ◽

2016 ◽

Vol 8 (1) ◽

pp. 013302 ◽

Cited By ~ 15

Author(s):

Yuzhang Che ◽

Xindong Peng ◽

Luca Delle Monache ◽

Takayuki Kawaguchi ◽

Feng Xiao

Keyword(s):

Wind Power ◽

Kalman Filtering ◽

Wind Farm ◽

Weather Research And Forecasting ◽

Forecasting Model ◽

Wind Power Forecasting ◽

Forecasting System ◽

Power Forecasting ◽

Weather Research

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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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An Advanced Cascaded Method for Regional Wind Power Forecasting

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.198-199.814 ◽

2012 ◽

Vol 198-199 ◽

pp. 814-818

Author(s):

Jin Sha Yuan ◽

Hong Yang

Keyword(s):

Wind Power ◽

Prediction Error ◽

Wind Farm ◽

Evaluation Criteria ◽

Wind Farms ◽

Practical Method ◽

Wind Power Forecasting ◽

Forecasting Models ◽

Simulation Results ◽

Power Forecasting

Regional wind power forecasting deals with the prediction of the aggregated power output of wind farms located within a defined region. By architecture, regional forecasting models are classed by three approaches. In these approaches, the cascaded approach is a most practical method although the method has a larger prediction error. This paper provides a systematic review of all these regional forecasting approaches and ultimately proposes an advanced cascaded method to improve forecasting precision. The method use root mean square error as evaluation criteria to adjust every prediction of signal wind farm in a region. The simulation results show the effectiveness of the advanced cascaded method.

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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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