Before starting to make statistical analysis, we have to answer the following questions:

**Which external variables can be related with the energetic consumption? Can I dispose of this data?**

Depending on the data we can get, the error in our regressions will be higher or lower. Keep in mind the cost that may involve get some of the data needed.

Below we can see a matrix of the most influential external variables can affect the energy consumption. Obviously, each project can be influenced by other variables. The user must identify them based on the facility and the experience.

**Which is the demonstrative period of activity in my facility?**

We have to know which is the period of time with a representative cycle of activity in our facility in order to know which is the minimum period of data we should collect before make our baseline. Here, we have some examples:

**Data preprocessing**

Once we have determined the variables are going to be correlated with the energetic consumption, we export them of DEXCell Energy Manager and we adequate them for the statistical software we are going to use (EXCEL, Minitab, Stata, Matlab, etc.)

We should remember that there is interaction between different variables or a variable can be representative if it is squared or cubed. We recommend to prepare additional data sets among variables and power calculations.

Moreover, we eliminate those anomalous or unrepresentative data of the installation.

**Get the mathematical formula**

The mathematical formula is the result of a linear/nonlinear statistical regression. Each of the software procedures performed variously estrus, so we must follow their own tutorials to get the formula.

Each project has its own mathematical formula and there not exist "standard" formulas for a type of installation.

HOWTO - Example of baseline calculation using Minitab 16

**Get the statistical error**

All statistical process involves a miscalculation. The procedure that we have selected for the calculation of the formula will give a correlation coefficient which will indicate the amount of points explained in the formula

Normally, the indicator is R^ 2.

If we got an R^2 = 98%, this indicates that our formula explains 98% of our consumption, so we will be making an error of 2%.

**Introducing conditionals**

Maybe, our baseline changes for an specific days or periods of time. We can introduce some conditions to change our formula by another one based on day of a week or months of a year. You can introduce any conditions as you need. These will be applied by priority order based on definition.

**Insert the formula in our Measure & Verification Project**

After calculating the formula and the error, we introduce our data in Measure & Verification project and we will have configured our savings for real-time reporting!