adenikeadewumi/Techno-Tribe

Real-time prediction of Nigeria's national grid collapse. # ⚡ AI for Grid Collapse Prediction This project was carried out as part of the **Deep Learning Indaba Community Challenge 2025**. It focuses on leveraging **Artificial Intelligence (AI)** and **Machine Learning (ML)** to build a system capable of predicting the **probability of national grid collapse** based on historical data. --- ## 🌍 Project Overview Power grid collapse is a critical issue in many developing nations, including Nigeria, leading to widespread blackouts, economic losses, and safety risks. This project explores how AI can be used to **anticipate grid instability** and help authorities take **proactive measures** to prevent such collapses. The ultimate goal is to provide a data-driven solution that can: - Detect potential patterns leading to grid failure. - Predict the **likelihood of grid collapse** at specific times. - Support **decision-making** for improved power infrastructure management. --- ## 🧩 Data Generation Due to the **limited availability of real-world national grid data**, this project uses **synthetically generated data** to simulate realistic grid behaviors over time. The dataset was designed to reflect: - Hourly and daily variations in power demand and supply. - Frequency fluctuations and voltage irregularities. - System load patterns under different operational conditions. Although synthetic, the data was modeled to maintain realistic statistical characteristics, enabling valid experimentation and learning. --- ## 🔬 Methodology ### 1. **Data Preparation** - Generated data was **cleaned**, normalized, and structured into **hourly** and **daily** subsets. - Feature engineering was applied to derive key indicators of grid health. ### 2. **Unsupervised Learning (Initial Phase)** - **Clustering** techniques were tested using **Principal Component Analysis (PCA)** to identify latent patterns in the grid behavior. - However, unsupervised learning resulted in **low prediction accuracy**, indicating the need for supervised appr …