Abstract
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.
Highlights
The contribution of wind energy to the electricity production portfolio is increasing compared to other productions with energy sources, such as nuclear, coal, hydroelectric, oil and gas, and biomass plants [1,2]
The Prediction Intervals (PIs) obtained by the BS technique are analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction
The work presented in this paper focuses on the quantification of the uncertainty of wind energy predictions provided by an ensemble of data-driven models
Summary
The contribution of wind energy to the electricity production portfolio is increasing compared to other productions with energy sources, such as nuclear, coal, hydroelectric, oil and gas, and biomass plants [1,2]. The work presented in this paper focuses on the quantification of the uncertainty of wind energy predictions provided by an ensemble of data-driven models. The quantification of the uncertainty affecting the wind energy production predictions provided by an ensemble of ANNs models employing BS technique; The comparisons with the Quantile (adopted by the plant owner) and MVE (from the literature) techniques used for the uncertainty quantification; The analysis of the BS’s PIs in terms of various influencing factors, such as the different weather conditions experienced by the wind plant and the time horizons of the predictions.
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