Confidence interval of wind power generation hours

How reliable is the wind power prediction interval?

Wind power prediction interval under 95% confidence level for different models. In Table 2, the coverage of the six models reach 95%, which shows that the reliability of the six models are relatively high. On the premise of meeting the coverage rate, the ΔP¯means the size of the interval width.

Can predictive intervals reduce wind randomness malfunction?

Pinson et al. [31]applied predictive intervals to assess the risk and uncertainty of wind power, which reduce wind randomness malfunction and provide more comprehensive information for decision makers to promote wind power bidding and transactions. Some scholars have also made various improvements on the PIWP model.

What is prediction interval of wind power (piwp)?

Prediction interval of wind power (PIWP) is crucial to assessing the economic and safe operation of the wind turbine and providing support for analysis of the stability of power systems.

What are the different types of wind power prediction methods?

In general, wind power prediction methods can be classified into two categories. One is point wind power prediction (PWP). The other is prediction interval of wind power (PIWP). The traditional methods for PWP fall into three types: physical models, statistical models, and artificial intelligence models.

Is beta-PSO-LSTM model good for wind power prediction?

From comparison results, the four performance indexes obtained by the Beta-PSO-LSTM model are the best in the six models, which proves that using the Beta-PSO-LSTM model to estimate the prediction interval of wind power can obtain higher coverage and high-quality prediction interval with narrow interval bandwidth.

How to evaluate the quality of interval forecast?

The value Fintegrates the two factors (the confidence interval width and the interval forecast accuracy) and comprehensively evaluates the quality of interval forecast. The two contradictory indexes are considered comprehensively. The value Fgives us an easy method to evaluate the quality of interval forecast.