Techno-economic optimization and hybrid deep learning-based net-load forecasting for intelligent microgrid energy management using HOMER Pro

This project framework guides the design, techno‑economic optimization, and short‑term forecasting of hybrid microgrids. It combines HOMER Pro simulations with modern deep‑learning models to tackle the difficulties of adding renewable energy. First, three different microgrid layouts are built and tested. Each layout includes: – photovoltaic (PV) panels – battery energy‑storage systems – diesel generators – a connection to the main grid The three designs are compared using economic, environmental, and operational criteria to pick the best one. After the optimal layout is chosen, a hybrid forecasting system is set up. It uses recurrent neural networks to predict, over the next few hours, both the building’s electricity demand and the PV

output. By merging these two forecasts, the system creates a net‑load profile that tells you how much power will be needed from the battery or the grid. This information supports decisions on when to charge or discharge the battery and how to interact with the grid. The approach gives a clear, step‑by‑step way to test how forecast‑driven strategies can reduce grid instability and lower operating costs. It also serves as a detailed technical reference for students who want to simulate and evaluate intelligent microgrid energy‑management systems, linking long‑term planning with short‑term operational forecasts.

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