Profitability Prospects for Residential Photovoltaic Installations in Honduras under Foggy Conditions: A Machine Learning Approach to Photovoltaic Production Prediction using Sarima and Linear Regression

This study takes a close look at whether solar panels are a good investment for homes in Honduras, especially in areas with foggy weather. Using a new approach, it uses Machine Learning (ML) to predict how much energy the panels will produce. It takes into account things like how much energy they will produce, how much they cost to install and maintain, and how much they can save on electricity bills. The results show that even though the sun doesn’t shine as much in some areas, solar panels are still a good way to save money. This study helps us to understand more about how to use solar energy in Honduras and gives valuable information to people who make decisions in government, business, and homes. It also shows how ML, especially the SARIMA (Seasonal Autoregressive Integrated Moving Average) model, helps predict how much energy solar panels will produce under different weather conditions, as the method used allows for addressing seasonal patterns due to variations in solar radiation throughout the year and is capable of effectively modeling these seasonal fluctuations.