A novel approach to ultra-short-term wind power prediction based on feature engineering and informer

Wang, Hui; Wang, Wensheng; Kao, Xiaoxuan

doi:10.1016/j.egyr.2022.12.062

Cited by 34 publications

(9 citation statements)

References 40 publications

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“…Self-attention weighted summation of all values by computing p k j q i , a process that requires O L Q L K ) time complexity and memory usage, are the main factor that improves the predictive power. Previous research has shown that the weights of self-attention are potentially sparse [30], and Informer investigates the sparsity of self-attention using the Kullback-Leibler scattering, where the difference between the distribution of query: p k j |q j , and the uniform distribution q k j |q j can be measured in terms of KL dispersion:…”

Section: Probsparse Self Attentionmentioning

confidence: 99%

Transformer Fault Diagnosis Method Based on TimesNet and Informer

Zhang,

Yang,

Zheng

2024

Actuators

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Since the traditional transformer fault diagnosis method based on dissolved gas analysis (DGA) is challenging to meet today’s engineering needs, this paper proposes a multi-model fusion transformer fault diagnosis method based on TimesNet and Informer. First, the original TimesNet structure is improved by adding the MCA module to the Inception structure of the original TimesBlock to reduce the model complexity and computational burden; second, the MUSE attention mechanism is introduced into the original TimesNet to act as a bridge, so that associations can be carried out effectively among the local features, thus enhancing the modeling capability of the model; finally, when constructing the feature module, the TimesNet and Informer multilevel parallel feature extraction modules are introduced, making full use of the local features of the convolution and the global correlation of the attention mechanism module for feature summarization, so that the model learns more time-series information. To verify the effectiveness of the proposed method, the model is trained and tested on the public DGA dataset, and the model is compared and experimented with classical models such as Informer and Transformer. The experimental results show that the model has a strong learning ability for transformer fault data and has an advantage in accuracy compared with other models, which can provide a reference for transformer fault diagnosis.

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Section: Probsparse Self Attentionmentioning

confidence: 99%

Transformer Fault Diagnosis Method Based on TimesNet and Informer

Zhang,

Yang,

Zheng

2024

Actuators

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“…Using the fireworks approach, an LSTM neural network-based wind speed estimation model was improved (FWA). In an integrated predicting strategy, it is also advised for hyperparameter tuning [21]. FWA is used to improve the hyperparameters of an LSTM-based wind speed prediction model.…”

Section: Wind Speed Prediction Using ML Techniquesmentioning

confidence: 99%

A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

Reja,

Amin,

Tasneem

et al. 2023

Atmosphere

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The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study’s short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey’s SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered.

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“…Furthermore, Ref. [41] developed a novel method for ultra-short-term wind power prediction, addressing previous limitations through feature extractions. The approach shows promising results, improving prediction accuracy and addressing space-time complexity issues.…”

Section: Introductionmentioning

confidence: 99%

Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting

Ramadevi,

Kasi,

Bingi

2024

Fractal Fract

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Efficient integration of wind energy requires accurate wind power forecasting. This prediction is critical in optimising grid operation, energy trading, and effectively harnessing renewable resources. However, the wind’s complex and variable nature poses considerable challenges to achieving accurate forecasts. In this context, the accuracy of wind parameter forecasts, including wind speed and direction, is essential to enhancing the precision of wind power predictions. The presence of missing data in these parameters further complicates the forecasting process. These missing values could result from sensor malfunctions, communication issues, or other technical constraints. Addressing this issue is essential to ensuring the reliability of wind power predictions and the stability of the power grid. This paper proposes a long short-term memory (LSTM) model to forecast missing wind speed and direction data to tackle these issues. A fractional-order neural network (FONN) with a fractional arctan activation function is also developed to enhance generated wind power prediction. The predictive efficacy of the FONN model is demonstrated through two comprehensive case studies. In the first case, wind direction and forecast wind speed data are used, while in the second case, wind speed and forecast wind direction data are used for predicting power. The proposed hybrid neural network model improves wind power forecasting accuracy and addresses data gaps. The model’s performance is measured using mean errors and R2 values.

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A novel approach to ultra-short-term wind power prediction based on feature engineering and informer

Cited by 34 publications

References 40 publications

Transformer Fault Diagnosis Method Based on TimesNet and Informer

Transformer Fault Diagnosis Method Based on TimesNet and Informer

A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey

Hybrid LSTM-Based Fractional-Order Neural Network for Jeju Island’s Wind Farm Power Forecasting

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