Synthetic wind speed time series generation by dynamic factor model

Mário Jorge Cardoso de Mendonça, José Francisco Moreira Pessanha, Victor Andrade de Almeida, Luiz Alberto Toscano Medrano, Julian David Hunt, Amaro Olímpio Pereira Junior and Erika Carvalho Nogueira

Renewable Energy, 2024, vol. 228, issue C

Abstract: The global transition to renewable energy is driven by the fight against climate change. Wind power plays a crucial role in reducing dependence on fossil fuels and greenhouse gas emissions. Therefore, addressing uncertainties in wind speed variations requires innovative solutions. This study proposes a Bayesian-based approach using a Dynamic Factor Model to generate synthetic monthly average wind speed series. The Dynamic Factor Model framework captures temporal and spatial correlations, improving wind resource representation in operational planning models. The model's autoregressive configuration with common factors, prior distributions, and Bayesian inference techniques enhances predictive capabilities. Validation exercises confirm the model's reliability, accurately capturing seasonal oscillations and spatial correlations across eight wind farms. The study highlights the usefulness of the Dynamic Factor Model in evaluating wind projects and optimizing energy generation strategies, effectively mitigating wind uncertainty, and facilitating renewable energy integration in Brazil's power mix.

Keywords: Dynamic factor model; Bayesian approach; Synthetic time series; Wind power; Power systems operation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:228:y:2024:i:c:s0960148124006591

DOI: 10.1016/j.renene.2024.120591

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