In 2018, solar-produced energy accounted for 1.6% of the electricity generated in the U.S., up from 0.11% in 2012. By 2020, solar-produced energy is forecasted to account for 5% of U.S. generated electricity. As a result of changing energy sources, energy generation and distribution methods have been continuously evolving over the last few years. With the increased efficiency associated with solar energy production and distribution, local homeowners have also assumed the role of energy generators, even getting credit for access electricity supplied to the grid given the policy around net-metering. When planning their energy distribution frameworks, utility companies have to take these changes in energy consumption and generation into account when planning their energy distribution frameworks. However, little is known about how solar energy systems impact the demand and supply of grid electricity managed by utility companies. Currently available products, such as PVWatts, SAM, and Google’s Project Sunroof, have offered energy forecasts and and return-on-investment estimates from a long-term 25-year perspective using historical weather data. However, the accuracy of these tools decreases when considering shorter time-scale forecasts. Devising methods and tools which can accurately estimate near-future (5-10 days) local weather forecasts and its implications for changes in solar energy production will positively impact utility companies and grid, reducing stakeholder costs currently associated with forecasting error. This project will showcase a model designed to incorporate localized historical weather data, real-time weather data, and near-future forecasted weather data to predict site-specific weather parameters for the purpose of estimating solar energy generation.