A unique three-step weather data approach in solar energy prediction using machine learning

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dc.contributor.authorFalope, Tolulope
dc.contributor.authorLao, Liyun
dc.contributor.authorHanak, Dawid P.
dc.date.accessioned2022-07-19T08:15:58Z
dc.date.available2022-07-19T08:15:58Z
dc.date.issued2021-12-02
dc.description.abstractThe importance of renewable energy sources like solar energy in reducing carbon emissions and other greenhouse gases has contributed to an increase in grid integration. However, the intermittent nature of solar power causes reliability issues and a loss of energy balance in the system, which are barriers to solar energy penetration. This study proposes a unique three-step approach that identifies weather parameters with moderate to strong correlation to solar radiation and uses them to predict solar energy generation. The combination of an on-site weather station and a reliable local weather station produces relevant data that increases the accuracy of the forecasting model irrespective of the machine learning algorithm used. This data source combination is tested, along with two other scenarios, using the exponential Gaussian Process Regression machine learning algorithm in MATLAB. It was found to be the most effective algorithm with a Normalized Root Mean Square Error of 1.1922, and an R2value of 0.66.en_UK
dc.identifier.citationFalope TO, Lao L, Hanak D. (2021) A unique three-step weather data approach in solar energy prediction using machine learning. In: 13th International Conference on Applied Energy (ICAE2021), 29 November - 2 December 2021, Bangkok, Thailanden_UK
dc.identifier.issn2004-2965
dc.identifier.urihttps://doi.org/10.46855/energy-proceedings-9863
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18187
dc.language.isoenen_UK
dc.publisherInternational Conference on Applied Energyen_UK
dc.subjectrenewable energy sourcesen_UK
dc.subjectvariable renewable energyen_UK
dc.subjectmachine learningen_UK
dc.subjectsolar energy forecastingen_UK
dc.subjectGaussian Process Regressionen_UK
dc.titleA unique three-step weather data approach in solar energy prediction using machine learningen_UK
dc.typeArticleen_UK

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