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

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.