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Questions & Answers

Q - I am making a multiple regression for C EVO model. Constant values (m1,m2 and m3) according to the equation y(consumption)= m1(days)+m2(activity hours)+ m3(HDD)+b results with negative values; What should I do?

1.- If constant b<0 ; is it possible the building had negative consumption if no other variables affect?
2.- If constant m1<0
3.- If constant m2<0
4.- If constant m3<0

What is the procedure in these cases?

A - Unless there is energy production (e.g. solar panels) within the measurement boundary, the estimated consumption should never be less than zero.

The coefficients should always make physical sense. Without know the full meeting of your equation, I would expect m1 and m2 to be positive. For example, it wouldn't usually make sense that energy use could go down as the number of days increased.

m3 should usually be positive. Possible issues include an incorrect base temperature for HDD or allowing negative values for HDD.

Last, but not of least importance, you should not be extrapolating outside the range of independent variables in your data. If you extrapolate, in some cases you can get negative estimates of energy use. For high frequency data (e.g. daily or hourly) we are having discussions about if or when minor extrapolation could be allowed, but right now IPMVP does not allow extrapolation. (It is certainly possible that a reporting period could have a month with slightly more (or fewer) HDD than was present in any month in the baseline even with a full year baseline, and so even at present it might make sense to have VERY MINOR extrapolation, but it should be avoided.

The CMVP course online is currently available only for US citizens. For all other countries the course is available only on site. In both cases, the CMVP exam is to be taken on site and cannot be done online.

The CMVP exam is not available online. You should attend the exam session after any of the trainings but you should register in advance. You are allowed to attend the exam within three years after the training.