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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Ranjan Nayak, Soubhagya
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Computer Science & IT

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Optimizing solar energy forecasting and site adjustment with machine learning techniques Prasad Mishra, Debani; Kumar Sahu, Jayanta; Ranjan Nayak, Soubhagya; Panda, Anurag; Paramjit Dash, Priyanshu; Reddy Salkuti, Surender
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp384-392

Abstract

Estimation of solar radiation is a key task in optimizing the operation of power systems incorporating high levels of photovoltaic (PV) generation. This paper discusses the application of machine learning techniques, namely extreme gradient boosting (XGBT) and random forest (RF), to improve accuracy in the forecasting of solar radiation while adapting for different sites. Utilizing datasets such as meteorological and solar radiation data, the suggested models demonstrate the enhancement of forecasting accuracy by 39% from traditionally applied statistical practices. Along with this, this study also encompasses how endogenous and exogenous factors could be involved in better predictions of solar energy availability. From our findings, XGBT, as well as other machine learning techniques, do enjoy superior performance levels when it comes to the forecasting of solar radiation, which in turn promotes efficient management and potential adaptation of solar energy systems. This study demonstrates how this last generation of algorithms could be applied to noticeably improve the efficiency of solar power forecasting and thereby contribute to more sustainable and reliable energy systems as a byproduct of that.
Co-Authors Kumar Sahu, Jayanta Panda, Anurag Paramjit Dash, Priyanshu Prasad Mishra, Debani Reddy Salkuti, Surender