Forecasting of Wind Turbine Output Power Using Machine learning

Rashid, Haroon; Haider, Waqar; Batunlu, Canraş

doi:10.1109/acit49673.2020.9208852

Cited by 15 publications

(10 citation statements)

References 3 publications

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“…Typically, the SCADA system gathers data from wind farms every 10 minutes to save data bandwidth. [31][32][33] Almost all modern wind farms use SCADA systems to record several critical parameters. The important measurements taken include (1) Active power, which provides data on the energy generated by the wind turbine.…”

Section: Scada Data Description and Analysismentioning

confidence: 99%

“…The measurements were made as a time‐series data set that records the data from December 2017 to March 2020 at intervals of 10 min. Typically, the SCADA system gathers data from wind farms every 10 minutes to save data bandwidth 31–33 . Almost all modern wind farms use SCADA systems to record several critical parameters.…”

Section: Recurrent Neural Network For Wind Power Prediction Using Sca...mentioning

confidence: 99%

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Performance enhancement of wind power forecast model using novel pre‐processing strategy and hybrid optimization approach

Kumar,

Prabhakar,

Verma

2023

Adaptive Control & Signal

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SummaryDue to the energy crisis and environmental concerns, wind power has seen a considerable increase in use over the past 10 years as a source of renewable energy. Since wind is intermittent and variable, it is obvious that as penetration levels rise, the influence of wind power generation on the electric power system must be considered. Wind power forecasting is essential because large‐scale wind power integration will make it more difficult to plan, operate, and control the power system. An accurate forecast is an efficient way to deal with the operational problems brought on by wind variability. To better utilize wind energy resources, it is essential to increase prediction accuracy. Frequently, the noise in the dataset causes the ramp events to be misclassified or overestimated. The main emphasis of this study is the pre‐processing of wind power data that produces precise time‐series data while reducing noise or artifact content and maintaining the swing feature of the original data. For this task, the data augmentation approach is proposed, where the augmented data (synthetic data) will be added up with the training data, in such a way as to make the strategy useful for real‐time applications. As the next step, the significant features, such as higher‐order statistical features and lower‐order statistical features are extracted. The extracted features act as the input to the recurrent neural network (RNN) classifier, the weights of which are tuned using the proposed honey badger crow (HBCro) optimization algorithm. The proposed HBCro optimization algorithm acts as the major contribution of the proposed model, and it is modeled with the integration of the concepts of the crow search optimization (CSO) algorithm and the honey badger optimization algorithm (HBA). The proposed system is simulated in MATLAB and the effectiveness of the proposed method is validated by comparison with other conventional methods in terms of Error measures. Furthermore, the developed HBCro‐based RNN obtained efficient performance in terms of MSE, MAE, RMSE, RMPSE, MAPE, MARE, MSRE, and RMSRE of 0.1578, 0.0442, 0.2102, 123.238, 124.72, 0.9944, 1.1799, and 1.0732, respectively.

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Section: Scada Data Description and Analysismentioning

confidence: 99%

Section: Recurrent Neural Network For Wind Power Prediction Using Sca...mentioning

confidence: 99%

Performance enhancement of wind power forecast model using novel pre‐processing strategy and hybrid optimization approach

Kumar,

Prabhakar,

Verma

2023

Adaptive Control & Signal

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“…Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation coefficients are commonly used to assess model effectiveness. "Forecasting of Wind Turbine Output Power Using Machine learning"by Haroon Rashid (2019) is an example study that demonstrates the application of these metrics [3].Optimizing model performance involves feature selection techniques and hyperparameter tuning. Researchers explore methodologies to identify the most influential features and optimize model parameters for improved accuracy in power output predictions.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Forecasting Models for Wind Turbine Power Output

Krishna,

Mishra

2023

Int. J. Res. Publ. Rev.

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Wind power is unpredictable due to chaotic nature of wind speed. This makes it important to accurately predict how much power a wind farm will generate.Predicting wind power helps balance the electricity supply and demand, which is very important for a stable power grid. These predictions also help other conventional power plants to mitigate the load demand. It also helps us trade electricity more efficiently with profitable cost. In this paper, we look at different methodologies to predict wind power. It starts by discussing how we use various algorithms to make predictions most accurately with less error. Using statistical and machine learning approaches we can able to get the solution in most precise way. Recent methodologies are the hybrid network models which have good prediction accuracy with less error. These all algorithms work on past data in large quantities which we are collecting from different wind farms. The paper gives a comparative analysis of various literatures and described which methodology is the best among all depending on various seasonal data and ultra short-term prediction.

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“…In order to reduce data bandwidth, the SCADA system typically collects data from wind farms every 10 min. 25,26,27 Almost all current wind farms use SCADA systems to record a variety of vital parameters.…”

Section: Deep Neural Network For Wind Turbine Power Prediction Using ...mentioning

confidence: 99%

Wind turbine power prediction via deep neural network using hybrid approach

Ahilan

Sujesh²,

Yarrapragada

2022

Proceedings of the Institution of Mechanical Engineers, Part A:

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Due to the chaotic nature of wind speed, short-term wind power prediction is a challengeable one. A reliable and accurate wind power prediction model is necessary for the wind turbine industry. Based on their improved ability to cope with complicated nonlinear issues, an increasing number of deep learning-based models are being explored for wind power prediction as artificial intelligence technologies, especially in deep neural networks. In this research, the wind power prediction model is divided into three stages. The first stage is to collect data from wind turbines. The second stage is to apply the optimal tuning of the Deep Neural Network (DNN) to predict the wind power. Here, the hybrid algorithm termedBird Swarm Merged Seagull Optimizer(BSMSO) stipulates DNN’s weight optimization points for wind power prediction; in addition, it reduces the time required for the same. Finally, the efficacy of the proposed BSMSO-DNN prediction model is proved by matching the statistical performance measures as regards toerror metrics with other existing techniques. The simulation results reveal thatthe proposed hybrid modelreduces the prediction errors significantly.

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Forecasting of Wind Turbine Output Power Using Machine learning

Cited by 15 publications

References 3 publications

Performance enhancement of wind power forecast model using novel pre‐processing strategy and hybrid optimization approach

Performance enhancement of wind power forecast model using novel pre‐processing strategy and hybrid optimization approach

Machine Learning-Based Forecasting Models for Wind Turbine Power Output

Wind turbine power prediction via deep neural network using hybrid approach

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