Recent Developments in Estimation of Solar Radiation

Choudhary, Amar; Pandey, Deependra; Bhardwaj, Saurabh

doi:10.1109/gucon48875.2020.9231215

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…In order to estimate global solar radiation (GSR), solar radiation estimating models are created, taking into account geographical and climatic parameters due to the constraints of current monitoring sites. [11] Turkey's growing solar energy potential was demonstrated in 2011 when the country's solar installations reached 442.13 MW thanks to a government support program. For industrial applications, the study calculates costs, payback times, and scenarios.…”

Section: Literature Surveymentioning

confidence: 99%

Breakeven Estimation of Solar Energy: A Machine Learning and Time Series Analysis Approach

et al. 2024

IJFMR

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This study focuses on determining the breakeven point for solar panel installations, utilizing a comprehensive dataset with daily information like sunrise, sunset, and temperature. Users input parameters such as panel area, location, current cost, and installation charges. Using advanced time series analysis, the system considers environmental factors, energy potential, and local prices to predict when cumulative income will surpass installation costs. This analysis is crucial for individuals and organizations assessing the financial viability of solar investments. By providing insights into profitability timelines, stakeholders can make informed decisions, promoting a sustainable transition to renewable energy. The study aligns with the broader goal of reducing reliance on non-renewable sources and fostering environmentally responsible practices.

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Section: Literature Surveymentioning

confidence: 99%

Breakeven Estimation of Solar Energy: A Machine Learning and Time Series Analysis Approach

et al. 2024

IJFMR

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Prediction and efficient installation of solar panel using machine learning

Sujith,

Ashwith,

Sai Kiran reddy

et al. 2024

MATEC Web Conf.

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Utilizing a comprehensive dataset featuring daily information such as sunrise time, sunset time, minimum and maximum temperatures, this study's primary aim is to ascertain the duration necessary for a solar panel installation to become profitable and reach the breakeven point. Users provide input parameters, including the solar panel area to be installed, location, cost per unit of current, and installation charges. Employing time series analysis and forecasting techniques, the system takes into account various environmental factors, energy generation potential, and local energy prices. The ultimate goal is to predict when the cumulative income from the solar panel installation will surpass the initial installation costs, facilitating an estimation of the point at which the solar panel system will break even and commence generating a net profit. This solar panel profitability analysis holds considerable significance individuals and organizations for contemplating investments in renewable energy. It equips stakeholders with the information required to make informed decisions regarding the feasibility and financial viability of solar panel installations at specific locations. This, in turn, promotes a sustainable and cost-effective transition to renewable energy sources, aligning with the broader goal of reducing our reliance on non-renewable energy and fostering environmentally responsible practices.

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Recent Developments in Estimation of Solar Radiation

Cited by 2 publications

References 27 publications

Breakeven Estimation of Solar Energy: A Machine Learning and Time Series Analysis Approach

Breakeven Estimation of Solar Energy: A Machine Learning and Time Series Analysis Approach

Prediction and efficient installation of solar panel using machine learning

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