The explosive growth of renewable energy has led to a higher need for tools that assist people in determining the viability and benefits of solar power installation. However, there are many potential users who do not have accessible platforms that integrate accurate solar generation prediction, financial analysis, and easy-to-use visualisation. This research introduces the AI-Assisted Solar Energy Generation Prediction System, a web-based application that approximates the solar energy generation based on the geographic location and the electricity consumption. The system combines several external data sources and the latest web technologies to deliver precise and interactive solar potential analysis. Geographic coordinates of chosen city are used to retrieve solar production estimates from the NREL PVWatts API and real time weather information is retrieved using the OpenWeather API. The backend, which was created with Python and Flask, takes these inputs and calculates solar generation, energy offset, financial savings, system coverage and environmental benefits such as CO2 reduction. The frontend interface is used to visualise the results in the form of interactive charts and comparison tables, allowing the user to compare different solar system sizes. In addition, the system includes an AI-based assistant, based on a large language model, to provide personalised insights and recommendations about solar installation. By integrating solar prediction algorithms, financial calculations and AI-generated analysis in one platform, the proposed system offers users an intuitive and informative decision support tool for solar energy adoption.
Solar Energy Prediction, Renewable Energy Systems, PVWatts API, Artificial Intelligence, Energy Consumption Analysis, Solar Power Estimation.
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