4  FM quick reads on Energy Model

1. Energy Modeling: What Happens When You Make Bad Assumptions?


Today's tip is about energy models, and what happens when you make bad assumptions. Energy models are only as good as the data put into them — that is, how close to reality the assumptions are made at the start of a project. In other words, the quality of the input data is ultimately what determines how useful the output data is. This is an especially cogent principle when it comes to an energy model, as the fewer assumptions the engineer doing the model must make, the more reliable the energy model will be.

The facility manager must have an open, honest discussion with the design team at the very beginning of the design process to make sure everything and everyone is on the same page. The information derived from this meeting - or more likely, these meetings... plural - will improve the quality of data input into the model. Inputs include window to wall ratio, tightness of air infiltration, thermal wall and roof insulation, performance criteria of glass, type and efficiency of mechanical system, lighting type and expected watts per square foot, plug loads watts per square foot, and occupancy.

Of course, there will always be some variables that are impossible to predict. The occupancy of the building may be greater than expected. The building's operating hours may be longer than expected. Equipment may not interact as expected. Even so, an energy model is still an essential (and if you're doing LEED, required) part of the construction process.

How many times have you heard that a LEED building is supbar because it used slightly more energy than its energy model predicted? That doesn't mean it's a bad building! After all, an energy model isn't intended to be a totally accurate predictor of the precise amount of energy used. That's what measurement and verification are for after the building opens. After building systems are commissioned, facility managers must measure and verify from the second the door are open in order to find anomalies and fix issues that may arise.


2.  Energy Modeling: What Happens When You Make Bad Assumptions?

Today's tip is about energy models, and the notion that they're only as good as the data put into them — that is, how close to reality the assumptions are made at the start of a project. In other words, the quality of the input data is ultimately what determines how useful the output data is. This is an especially cogent principle when it comes to an energy model, as the fewer assumptions the engineer doing the model must make, the more reliable the energy model will be.

The facility manager must have an open, honest discussion with the design team at the very beginning of the design process to make sure everything and everyone is on the same page. The information derived from this meeting - or more likely, these meetingsā€¦plural - will improve the quality of data input into the model. Inputs include window to wall ratio, tightness of air infiltration, thermal wall and roof insulation, performance criteria of glass, type and efficiency of mechanical system, lighting type and expected watts per square foot, plug loads watts per square foot, and occupancy.

Of course, there will always be some variables that are impossible to predict. The occupancy of the building may be greater than expected. The building's operating hours may be longer than expected. Equipment may not interact as expected. Even so, an energy model is still an essential (and if you're doing LEED, required) part of the construction process.

How many times have you heard that a LEED building is supbar because it used slightly more energy than its energy model predicted? That doesn't mean it's a bad building! After all, an energy model isn't intended to be a totally accurate predictor of the precise amount of energy used. That's what measurement and verification are for after the building opens. After building systems are commissioned, facility managers must measure and verify from the second the door are open in order to find anomalies and fix issues that may arise.


RELATED CONTENT:


Energy Model , Data , LEED

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