Abstract
In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behavior of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the outperformance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.
Highlights
Due to the continued increase of energy demand, conventional energy sources seem unable to support energy advances in recent years
Considering increasing global energy consumption and the anticipated rise of energy demand, there is a need for renewable energy sources to support sustainable advancement in major energy suppliers
Wind power is considered an environmentally sustainable source of renewable energy with less significant attention given to its utilization
Summary
Due to the continued increase of energy demand, conventional energy sources seem unable to support energy advances in recent years. Countries have set a long-term goal for the utilization of renewable energy sources (Zhang et al, 2018). According to (Bahrami et al, 2019), 121 out of 195 nations use it as a source of electricity with Asia accounted for almost 40%. The authors’ specific focus was on promoting the renewable energy exploitation in Turkmenistan by providing the country's first wind speed evaluation. The results highlight the significance of the energy market as Turkmenistan is a main electricity supplier in the Central Asia and its further potential from wind power. Wind speed is a significant element in the wind power production (Hu et al, 2021), and accurate and dependable wind speed and weather forecasting systems are conducive to lowering operating costs and improving wind power system stability (Zhang et al, 2020). Scholars are currently undertaking substantial research and contributing significantly to the area of wind speed forecasting
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