2022
DOI: 10.1016/j.egyr.2021.10.102
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A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data

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Cited by 50 publications
(16 citation statements)
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“…The direct influence of wind speed on the energy power curve of wind turbines and the stochastic and intermittent nature of wind have made its prediction a recurring research topic [14]. According to the type of techniques used, metocean (meteorological and oceanographic) variables forecasting models can be classified into (1) naive, (2) physical, (3) statistical and (4) intelligent models [15,16].…”
Section: Related Workmentioning
confidence: 99%
“…The direct influence of wind speed on the energy power curve of wind turbines and the stochastic and intermittent nature of wind have made its prediction a recurring research topic [14]. According to the type of techniques used, metocean (meteorological and oceanographic) variables forecasting models can be classified into (1) naive, (2) physical, (3) statistical and (4) intelligent models [15,16].…”
Section: Related Workmentioning
confidence: 99%
“…The datasets messiness evokes a separate research subfield, data pre-processing, and various pre-processing techniques are used, for example, in order to eliminate outliers 159 or classify different weather types. 160 None of the datasets that are presented in Table 2 is described as being pre-processed. We, therefore, assume that the datasets contain non-processed data.…”
Section: Data Qualitymentioning
confidence: 99%
“…However, incomplete and noisy data usually represent the data measured in the real world better than consistent and complete datasets, although we usually attribute a higher quality to the latter. The datasets messiness evokes a separate research subfield, data pre‐processing, and various pre‐processing techniques are used, for example, in order to eliminate outliers 159 or classify different weather types 160 . None of the datasets that are presented in Table 2 is described as being pre‐processed.…”
Section: Data Qualitymentioning
confidence: 99%
“…Vladislavleva et al (2013), (Wu et al, 2021), and (Taylor et al, 2009) used output predictions based on weather data to analyze relevant parameters and their correlation with energy output. He et al (2022) proposed a short-term WPF model based on NWP analysis (He et al, 2022). Several factors were selected from the NWP multivariate data by using the criteria of the minimum redundancy maximum correlation (MRMR) algorithm, and the weather patterns were divided into different types according to these characteristics.…”
Section: Introductionmentioning
confidence: 99%