Email this article Classification and Dynamics of Solar Radiation Regimes Using Multi-State Hidden Markov Models
Abstract
One of the key climatic factors affecting the production of renewable energy, especially photovoltaic (PV) power, is solar radiation. Using Multi-State Hidden Markov Models (HMMs), the classification and dynamics of solar radiation regimes are examined in this work. Hidden solar radiation states belonging to low, moderate, and high energy regimes were identified and their transition dynamics across time were analysed using the HMM framework. The results revealed that solar radiation dynamics in the study region are better represented by a three-regime structure rather than a two-regime classification. The shifts between the states highlight the dynamic nature of renewable energy sources. Low and moderate radiation states were more common than high-radiation regimes, according to frequency distribution analysis. State 1 exhibits the highest energy potential (ranging between 0.70 and 0.90) in Niger, Kwara, Nasarawa, and FCT, but the lowest in Benue and Kogi. These results demonstrate the value of Hidden Markov Models in comprehending the stochastic dynamics of solar radiation and enhancing grid integration, renewable energy forecasting, and sustainable energy management. The study confirms that the region possesses significant renewable energy potential despite observed atmospheric inconsistency and recommends the incorporation of hybrid renewable systems and energy storage technologies to improve energy reliability.
Citation: Agada, I., Peter, O., & Adah, V. (2026). Classification and Dynamics of Solar Radiation Regimes Using Multi-State Hidden Markov Models. Trends in Renewable Energy, 12(1), 122-139. doi:http://dx.doi.org/10.17737/tre.2026.12.1.00201
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DOI: http://dx.doi.org/10.17737/tre.2026.12.1.00201
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This work is licensed under a Creative Commons Attribution 4.0 License.Copyright @2014-2026 Trends in Renewable Energy (ISSN: 2376-2136, online ISSN: 2376-2144)