AI Innovations in Energy Management

Written by Dr. Rachid Ejjami

This book delves deeply into AI-driven models and their application to real-world energy concerns. The first case study, AI-Driven Building Energy Consumption Forecasting, examines how machine learning algorithms use 51,001 energy records to forecast building energy use based on historical data, weather patterns, and occupancy trends. This predictive capability helps building managers and energy providers improve energy efficiency, lower costs, and make more informed decisions. The second case study, AI-Driven Energy Fraud Detection in Smart Meters, tackles the growing problem of fraudulent energy usage. Using a 44,316-record dataset, this study investigates how AI-powered models—including Logistic Regression, Decision Trees, and Random Forests—can detect abnormal energy consumption trends, identify meter tampering, and reduce utility revenue losses. This book combines industry-specific information with cutting-edge AI approaches, making it an invaluable resource for energy professionals, data scientists, AI researchers, and regulators looking to capitalize on AI's potential in energy optimization and fraud detection.

Format: PDF

ISBN: 978-2-9598416-4-4