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In the UtilityBill measure, the formulas for computing NMBE and CV-RMSE include an 'n-1' rather than just an 'n' in the denominator. For example:

Source: https://github.com/NREL/OpenStudio/blob/e603410ac3567d44e7660294f3b06a8a2d591118/src/model/UtilityBill.cpp#L375

<<<<< result = 100.0 * std::pow(squaredError / (n - 1), 0.5) / ybar; >>>>> <<<<< result = 100.0 * (sumError / (n - 1)) / ybar; >>>>>

Why doesn't the formula just use 'n' as described in the definition, e.g. https://www.kw-engineering.com/how-to-assess-a-regressions-predictive-power-energy-use/

Thank you

In the UtilityBill measure, the formulas for computing NMBE and CV-RMSE include an 'n-1' rather than just an 'n' in the denominator. For example:

Source: https://github.com/NREL/OpenStudio/blob/e603410ac3567d44e7660294f3b06a8a2d591118/src/model/UtilityBill.cpp#L375

<<<<< result = 100.0 * std::pow(squaredError / (n - 1), 0.5) / ybar; >>>>> <<<<< result = 100.0 * (sumError / (n - 1)) / ybar; >>>>>

Why doesn't the formula just use 'n' as described in the definition, e.g. https://www.kw-engineering.com/how-to-assess-a-regressions-predictive-power-energy-use/

Thank you

In the UtilityBill measure, the formulas for computing NMBE and CV-RMSE include an 'n-1' rather than just an 'n' in the denominator. For example:

Source: https://github.com/NREL/OpenStudio/blob/e603410ac3567d44e7660294f3b06a8a2d591118/src/model/UtilityBill.cpp#L375

result = 100.0 * std::pow(squaredError / (n - 1), 0.5) / ybar; >>>>>
   <<<<< ybar;
result = 100.0 * (sumError / (n - 1)) / ybar; >>>>>
ybar;

In the UtilityBill measure, the formulas for computing NMBE and CV-RMSE include an 'n-1' rather than just an 'n' in the denominator. For example:

Source: https://github.com/NREL/OpenStudio/blob/e603410ac3567d44e7660294f3b06a8a2d591118/src/model/UtilityBill.cpp#L375

result = 100.0 * std::pow(squaredError / (n - 1), 0.5) / ybar;
result = 100.0 * (sumError / (n - 1)) / ybar;

Why doesn't the formula just use 'n' as described in the definition, e.g. https://www.kw-engineering.com/how-to-assess-a-regressions-predictive-power-energy-use/

Thank you