ASIMONYE CHIDERA PATRICK

This work presents the development of an intelligent software solution for the design and optimization of solar energy systems. The system integrates key variables such as solar panel efficiency, geographic location, battery storage requirements, and peak load consumption to generate optimized configurations tailored to user needs. The tool supports both mobile and desktop platforms, providing an intuitive graphical user interface (GUI) for real-time analysis and seamless data input. The software features a comprehensive cost-benefit analysis module that compares the initial investment with projected long-term savings in energy costs. The software architecture is layered and modular. The GUI Layer enables users to simulate various solar system configurations, input energy requirements, geographic location, budget, and preferences for renewable energy components (e.g., solar panels, batteries, inverters). It provides actionable recommendations, displays optimized system designs, cost estimates, and generates detailed reports. The Data Layer consists of databases that store critical information, including solar irradiance data, energy consumption profiles, technical specifications of solar components, and pricing information. Data sources include third-party APIs, cloud storage, and local servers. The system also features a dynamic database of available solar products and real-time pricing updates through integrated APIs. The Computation and Simulation Layer simulates energy production, consumption, and storage across different scenarios, leveraging historical weather data and user consumption profiles to predict long-term system performance. Mathematical models were developed to establish the relationships among system components and to optimize system parameters for cost-effectiveness and reliability. These models were implemented using the Django framework and simulated via MATLAB. They form the core of the software's computation and optimization engine. The Optimization Engine, which utilizes advanced algorithms to compute the most efficient and cost-effective solar architecture. It processes user inputs, environmental data, and component specifications to determine optimal combinations of solar panels, batteries, and inverters. Lastly, the Back-End Services Layer manages the interaction between the user interface, optimization engine, and data storage. These services ensure smooth data flow, handle computation requests, and deliver results to the user in real time. The GUI was developed using the HTML, CSS and JavaScript’s framework, with MySQL used for database management. Designed for both residential and commercial applications, the tool streamlines decision- making for installers, energy consultants, and property owners. Its flexibility in accounting for various renewable energy sources and its detailed recommendations for system sizing and 5 installation positions it as a valuable resource for optimizing both performance and budget in solar energy projects. The software-optimized PV system configuration delivers significant performance and financial benefits, achieving 15–20% higher energy output and 90–95% battery utilization through intelligent energy management. By optimizing system design, it reduces upfront component costs by 10–15% and saves 8–10% on labor and materials during installation. End users experience 20–25% lower energy bills and 30–35% reduced grid dependency thanks to efficient load balancing and storage cycling, making it an ideal solution for both residential and commercial applications. Additionally, the tool provides key financial metrics such as return on investment (ROI), levelized cost of energy (LCOE), and payback period empowering informed decision-making. Field validation from over 150 survey respondents in Nigeria confirmed the tool’s user-friendliness and the strong market need for an integrated solar optimization platform. These results demonstrate a cost-effective, high- performance solution that enhances energy efficiency, cuts operational expenses, and accelerates the adoption of sustainable solar power