Master Energy and Sustainability Analytics: Machine Learning

Master Energy and Sustainability Analytics: Multiple Linear Regression, Machine Learning & CUSUM for IPMVP Option C

What you'll learn
- 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 onmonthly 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 insmart metering and digital monitoring, we have access tohigh-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 intotrusted, auditable resultsusingMultiple Linear Regression (MLR),Random Forest (ML),CUSUM,Time-of-Use (TOU) pricing, andgrid emission factorsaligned toIPMVP Option C.

This shift has created a new era in energy analytics. With high-frequency data, we can:

Seeimmediate responsesto changes in building operations

Capture the effect of occupancy, schedules, and weather in much greater detail

Identify subtle savings patterns that would never appear in monthly billing data

Provide stakeholders withtransparent, evidence-based reporting

But 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 wheremodern machine learningcomes 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 learnCUSUM analysis, a simple yet powerful tool for visualizing and communicating savings.

By the end of this course, you will:

Understandhow high-frequency energy data is collected from meters and why it matters.

Buildregression models to normalize for weather, occupancy, and other factors.

Applymachine learning methods to high-frequency data for improved accuracy.

UseCUSUM analysis to clearly visualize savings and performance trends.

Connectall analysis to theIPMVP Option Cframework for transparent verification.

Calculate GHG emissions savingsby converting kWh savings intoCO₂eusing grid emission factors (hourly or annual averages), andreport results in kg and tonneswith simple, auditable math.

This course is designed for energy analysts and engineers, facility managers, sustainability/ESG professionals, and data-curious learners who want practical, job-ready skills.

Energy efficiency isn’t just about saving kilowatts — it’s aboutproving 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 engineers

Sustainability and M&V professionals

Data enthusiasts interested in energy efficiency

Anyone seeking to bridge theory withpractical, 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
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