Abstract
For installation of solar photovoltaic system there will be adequate approaches required those mainly including solar radiation mapping, forecast, site evaluation, and potential assessment, to resolve such approaches there will be problem solving advanced techniques being evolved those are based on geospatial and machine learning technology. A review to make up the clarity related to the domain a specific role of these technologies as problem solving competency in relation to photovoltaic solar power systems (PV System), forecast and for the conversion of solar energy into electricity scenario. The review was performed by classifying previous Geospatial and Machine learning (ML) based studies according to the complexity of the active Geospatial and Machine Learning based techniques being utilized at extent to support for this stuff that is the data source, and the findings of the study area potential for more accurate assessment is required. The Geospatial technology is appropriate for handling location based and Machine Learning is more suitable for forecast related stuff but data related to solar resource and site suitability circumstances on various scales required. The claims of Geospatial Technology with Machine Learning based approaches in solar power system installation, planning and potential measure analysis of specific convinced technology, its role and truthfulness can be extended further.
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Sangle, A.S., Deshmukh, P.D. (2022). Forecasting Solar Energy on Time Frame: A Review. In: Rathore, V.S., Sharma, S.C., Tavares, J.M.R., Moreira, C., Surendiran, B. (eds) Rising Threats in Expert Applications and Solutions. Lecture Notes in Networks and Systems, vol 434. Springer, Singapore. https://doi.org/10.1007/978-981-19-1122-4_45
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