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Master Energy Analytics: Multiple Linear Regression And Ml
Published 9/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 54m | Size: 900 MB
Master Energy Analytics: Regression, Machine Learning & CUSUM for IPMVP Option C
What you'll learn[/center]

Build reliable regression models to establish energy baselines and normalize for weather, occupancy, and other factors.
Use CUSUM (Cumulative Sum) analysis to visualize energy savings and track performance trends.
Apply machine learning techniques to high-frequency energy data for more accurate savings predictions.
Understand how modern meters capture high-frequency data and why this data is essential for precise energy analytics.
Connect regression and machine learning results to the IPMVP Option C framework for credible measurement and verification.
Gain confidence in interpreting model performance metrics such as R², NMBE, and CV(RMSE) to evaluate baseline quality.
Requirements
Basic Excel skills are preferred but not required
Interest in energy analytics, sustainability, or data-driven decision-making
No prior programming or machine learning experience needed
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 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 communicating savings.By the end of this course, you will:Understand how high-frequency energy data is collected from meters and w***t mattersBuild regression models to normalize for weather, occupancy, and other factorsApply machine learning methods to high-frequency data for improved accuracyUse CUSUM analysis to clearly visualize savings and performance trendsConnect all of this to the IPMVP Option C framework for transparent verificationThis course is designed for energy analysts, engineers, and sustainability professionals who want to move beyond outdated, low-resolution methods and embrace the future of energy analytics.Energy efficiency isn't just about saving kilowatts - it's about proving impact with confidence, using the best data and the best tools available. High-frequency data is the modern fuel for that transformation, and machine learning is the engine that drives it.This course is designed for:Energy analysts and engineersSustainability and M&V professionalsData enthusiasts interested in energy efficiencyAnyone seeking to bridge theory with practical, hands-on analytics.
Who this course is for
Energy analysts and engineers
Sustainability and M&V professionals
Data enthusiasts interested in energy efficiency
Anyone seeking to bridge theory with practical, hands-on analytics
Anyone interested to use Machine Learning models for energy and sustainability analytics