Master Energy Forecasting: Zero to Hero

Partner: Udemy
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Description: The way we measure and verify energy performance is changing.In the past, energy analysis relied heavily on monthly utility bills. That was enough to provide a rough estimate of savings, but it lacked detail and often masked the true impact of energy efficiency measures. Today, thanks to advances in smart metering and digital monitoring, we have access to high-frequency data — hourly, 15-minute, even real-time energy use data streamed directly from modern meters.This course shows you—step by step—how to turn raw meter data into trusted, auditable results using Multiple Linear Regression (MLR), Random Forest (ML), CUSUM, Time-of-Use (TOU) pricing, and grid emission factors aligned to IPMVP Option C.This shift has created a new era in energy analytics. With high-frequency data, we can:See immediate responses to changes in building operationsCapture the effect of occupancy, schedules, and weather in much greater detailIdentify subtle savings patterns that would never appear in monthly billing dataProvide stakeholders with transparent, evidence-based reportingBut higher data resolution also brings complexity. Traditional regression methods may struggle to keep up with the volume and variability of high-frequency data. That’s where modern machine learning comes in. Techniques such as Multiple Linear Regression and Random Forest regression can handle nonlinear relationships and large datasets, giving analysts more accurate and flexible models.This course takes you on that journey — from building reliable regression baselines to applying machine learning for high-frequency data. Along the way, you’ll also learn CUSUM analysis, a simple yet powerful tool for visualizing and communicati
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Price: 29.99
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Source: Impact
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