A novel method based on lower–upper bound approximation to predict the wind energy

Wen, Peng; Zhang, Su; Xing, Yanfei; Hou, Limin; Bohlooli, Navid

doi:10.1016/j.jclepro.2020.120458

Cited by 9 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective is the quantification of the overall uncertainty, which affects the predictions provided by the ensemble. Various ANN-based methods have been developed and applied for the estimation of Prediction Intervals (PIs) of energy production predictions, such as Delta [45,46], Bootstrap (BS) [47][48][49][50], Lower Upper Bound Estimation (LUBE) [51][52][53][54], and Mean-Variance Estimation (MVE) [55,56]. For example, Khosravi et al [56] proposed an optimized MVE method for quantifying the uncertainty associated with the wind power predictions by constructing reliable PIs.…”

Section: Introductionmentioning

confidence: 99%

“…The estimated PIs were more informative than those obtained by the traditional MVE method for three wind farms located in Australia. Wen et al [52] proposed a novel method based on LUBE for predicting wind power production and quantifying the associated uncertainty in both hourly and daily modes. The quality of the estimated PIs obtained by the proposed method was superior to other PIs obtained by other benchmarks for different wind farms located in Taiwan.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production

et al. 2021

View full text Add to dashboard Cite

The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in the energy mix. However, different sources of uncertainty affect the predictions, providing the decision-makers with non-accurate and possibly misleading information for grid operation. In this regard, this work aims to quantify the possible sources of uncertainty that affect the predictions of wind energy production provided by an ensemble of Artificial Neural Network (ANN) models. The proposed Bootstrap (BS) technique for uncertainty quantification relies on estimating Prediction Intervals (PIs) for a predefined confidence level. The capability of the proposed BS technique is verified, considering a 34 MW wind plant located in Italy. The obtained results show that the BS technique provides a more satisfactory quantification of the uncertainty of wind energy predictions than that of a technique adopted by the wind plant owner and the Mean-Variance Estimation (MVE) technique of literature. The PIs obtained by the BS technique are also analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction.

show abstract