International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 2 March-April 2024 Submit your research before last 3 days of April to publish your research paper in the issue of March-April.

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

Author(s) Amancha Ashwith, Argula Sujith, Kallem Sai Kiran Reddy, Shruthi Kansal
Country India
Abstract 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.
Keywords Solar radiation, daily energy generation estimation, machine learning, random forest, time series analysis, ARIMA, SARIMA, renewable energy, energy price forecasting.
Field Computer > Data / Information
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-27
Cite This Breakeven Estimation of Solar Energy: A Machine Learning and Time Series Analysis Approach - Amancha Ashwith, Argula Sujith, Kallem Sai Kiran Reddy, Shruthi Kansal - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.12551
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.12551
Short DOI https://doi.org/gtghnb

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