Randomness of wind power generation prediction

Can predictive models predict wind speed in wind energy generation?

This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems.

How accurate is wind power forecasting?

The currently-used wind power forecasting (WPF) produces only a conditional expectation of wind power output, and is a deterministic prediction (or spot prediction) in nature. Until now, much research has been carried out for improving the accuracy of these prediction methods, which has been published in other reviews , .

Why is wind prediction error affected by hourly power generation?

The wind prediction error is affected by the hourly power generation because the prediction model is employed based on the irregular hourly wind output. In contrast, the solar prediction error is affected by daily fluctuations since solar generation exhibits daily periodicity.

How is wind power prediction based on data preprocessing?

Firstly, data preprocessing is conducted, and then the BOA-VMD method is used to decompose historical wind energy into K-IMFs. Subsequently, a prediction model is established for each IMF, and the prediction results are obtained using BOA-LSTM. Finally, the results are integrated to achieve the final wind power prediction.

What are probabilistic forecasts of wind power generation?

Probabilistic forecasts are the most used representation of the uncertainty in WPF, which is introduced in this section. The two other forms, i.e. risk index and spatial–temporal scenario, would be reviewed in 6 Risk index forecasting of wind power generation, 7 Wind power space-time scenario forecasting.

Does a wind power model improve accuracy in short-term wind power prediction?

The optimal weight and threshold can be automatically selected, reducing the randomness obtained from empirical selection and improving the accuracy of the model. The experimental analysis of actual wind power data shows that the model proposed in this paper has improved accuracy in short-term wind power prediction.