Course Contents

• Foundations of AI in ESP – Concepts, benefits, and trends in energy, sustainability, and power. AI Vision & Data Fundamentals – Data preprocessing, feature engineering, and platforms like Python. Ethics & Security – Responsible AI and data protection in power systems.
• Supervised Learning – Regression and classification techniques for ESP applications.
• Unsupervised & Advanced ML – Clustering, dimensionality reduction, ensembles, KNN, and SVM. Time Series & Forecasting – Energy data analysis with traditional and AI-driven models.
• Deep Learning Models – Neural networks, CNNs, RNNs, and LSTMs for power optimization. AI in Power Applications – Predictive maintenance, combustion efficiency, solar forecasting, and pump reliability.
• Business & Case Studies – Load forecasting, intelligent grids, generative AI, and project lifecycle. Capstone Project – Team-based applied AI challenge with real-world ESP datasets.