Prediction and efficient installation of solar panel using machine learning

Abstract

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.