Analyzing and Forecasting Energy Consumption with Python

Understanding energy consumption patterns is critical for managing resources, reducing costs, and planning for sustainable infrastructure. This text-based course guides you through the process of analyzing and forecasting energy data using Python, even if you are new to time-series analysis. You will transition from working with raw utility data to building reliable forecasting models. Through clear written explanations, step-by-step code walkthroughs, and practical exercises, you will learn how to identify trends, handle seasonality, and project future energy needs. What you'll learn: - Understand foundational time-series concepts like seasonality, trends, and stationarity - Clean and prepare raw energy consumption datasets using modern Python data libraries - Build and evaluate statistical forecasting models including SARIMA and Holt-Winters - Apply modern Python practices such as virtual environments and type hinting to organize your code - Compare model performance using standard error metrics to select the most accurate predictions - Visualize historical energy trends and future forecasts using written code snippets The course begins with essential terminology and data preparation techniques before moving into hands-on modeling. You will progress from basic moving averages to advanced seasonal forecasting models, building your confidence at each step. This course is designed for beginners, data analysts, and energy professionals who want to apply Python to time-series data. No prior forecasting experience is required, though a basic familiarity with Python variables is helpful. Start reading today to master the fundamentals of energy data forecasting.