A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation

Jingxuan Liu, Haixiang Zang, Fengchun Zhang, Lilin Cheng, Tao Ding, Zhinong Wei and Guoqiang Sun

Renewable Energy, 2023, vol. 219, issue P1

Abstract: Solar energy has received considerable attention worldwide because of its clean, safe and easily accessible characteristics. Accurate global solar radiation estimation is crucial for local energy potential assessments and deployment of photovoltaic power generation systems. However, the distribution of metrological data varies considerably between regions, and its quality considerably affects the accuracy of the estimation model. Motivated by these concerns, a metrological data estimation framework based on the time-series generative adversarial network (TimeGAN) and deep belief network (DBN) was proposed. First, the metrological data collected from 30 urban meteorological stations in China were analyzed through time-series aggregation. Next, a novel metrological data simulation model based on TimeGAN was proposed to enrich the quality of training samples. Furthermore, a generalized estimation model of daily solar radiation based on DBN was constructed. In the case study in China, TimeGAN outperforms other conventional generative adversarial networks in terms of data generation performance. The simulation results reveal that the TimeGAN-based simulation model can capture the inherent temporal characteristics of the original data, generate high-quality simulation data to reasonably improve the distribution of training data in various cities. In addition, the estimation results indicate that the proposed hybrid framework improve the accuracy of daily solar radiation estimation.

Keywords: Daily global solar radiation; Meteorological data simulation; Time-series generative adversarial network; Deep belief network (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:219:y:2023:i:p1:s0960148123012892

DOI: 10.1016/j.renene.2023.119374

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