Virtual CommissioningTM (VCxTM) is an innovative energy efficiency program which provides utilities
with a mass market small and medium business (SMB) solution by capitalizing on AMI investment. Want to learn more?
In the world of Measurement and Verification (M&V), proving the true ROI of an energy-saving project requires more than basic math. Relying on a simple “before and after” subtraction leaves your data vulnerable to real-world changes, like unexpected weather shifts or sudden drops in building occupancy. That is where multivariate regression modeling comes in. By factoring in multiple independent variables, a multivariate regression model calculates a highly accurate energy baseline equation, ensuring your M&V reports reflect precise, verifiable energy consumption and savings.
In Measurement and Verification (M&V), a multivariate regression model calculates the energy consumption (kWh, therms, kW, etc.) based on influential factors such as weather, occupancy, or production.
In this type of model, your consumption is the dependent variable and your influential factor is the independent variable. You are able to include multiple independent variables into a model to capture the true drivers of your energy use.
The regression model takes these factors into account when creating its baseline model equation. A baseline model equation simply states that there is a baseline level of consumption for every independent variable included.
For example, a school has energy consumption influenced by both outdoor temperature and building occupancy. In the school’s example, this baseline equation would mean that there is a baseline amount of kWh consumption per day, and that consumption increases or decreases depending on the changes in temperature and building occupancy.
Multivariate regression modeling is highly adaptable. It can be done at the whole facility level or for an isolated piece of equipment, depending on the scope of your project.
Ultimately, this modeling approach helps to reflect real-world conditions in your energy-saving project. This provides a rigorous and accurate standard of proof, as opposed to a simple subtraction of the consumption data before and after the measures are installed.