Solar Irradiation Data Analysis Using Python and SPSS for Rooftop PV Design Based on Energy Import Export to Reduce Household Electricity Bills - Aprilia Sarah Kristina; Rinaldy Dalimi (Green Publisher Indonesia, 2026)
Solar energy is a promising renewable resource in Indonesia due to the country’s high year-round solar availability. In rooftop photovoltaic systems, solar irradiation is a key factor affecting panel output, particularly on the DC side. This study addresses the limited integration of satellite-based irradiation analysis, technical data processing, statistical modeling, and household electricity consumption in a single framework for rooftop photovoltaic planning. The study aims to analyze the characteristics of solar irradiation in Depok City, estimate the DC output of a 400 Wp monocrystalline module, and simulate the contribution of rooftop photovoltaic systems to reducing grid-imported electricity and household electricity bills. A quantitative approach was applied using hourly NASA POWER data for January 2025 to January 2026 and actual residential electricity bill data from the same period. The irradiation dataset was cleaned and processed using Python, then analyzed in SPSS through descriptive statistics, assumption testing, correlation and simple linear regression. The results indicate that NASA POWER data can provide a reliable basis for estimating rooftop photovoltaic performance and that solar irradiation has a strong relationship with DC output. The simulation further shows that rooftop photovoltaic deployment can significantly reduce electricity imported from the PLN grid and lower household electricity costs under selected capacity scenarios. These findings support the use of integrated data driven methods for rooftop photovoltaic planning in urban residential settings.