Robust Estimation for Solar Radiation in Renewable Energy Systems
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DOI:
https://doi.org/10.59287/ijanser.1415Keywords:
Artificial, İntelligence, Machine Learning, Prediction, Solar Radiation, Renewable EnergyAbstract
Renewable energy is gaining huge respect due to the increase in demand for energy globally, low cost production, and sustainability of the environment. The one to mention out of all renewable energies is solar energy. Due to its availability in abundance. A wide range of people are driven to invest in this industry. Because of its high co-linearity, it's important that proper estimation of solar radiation is necessary for long-term performance, management of energy, and economic aspects. In this paper global horizontal irradiance is predicted from input parameters namely, air temperature, relative humidity, 5 wind speed, wind spend of ghust, Wind direction in degrees north counted clockwise (standard deviation), Wind direction in degrees north counted clockwise, day length, and air pressure. Based on these factors the paper investigates the performance of different machine-learning models for solar radiation estimation. R2(coefficient of determination) of the models is compared, and the best one, i.e., gradient boost regressor model is selected which predictssolar radiation in Pakistan from metrological data. Considering wind speed of gust and wind direction in a single model with high accuracy, and aids knowledge to predictions using machine learning models. The paper is part of an ongoing effort in the research community to predict solar radiation with ease and accuracy. As real-time data is difficult and costly due to high calibration costs.
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