What makes the AI algorithms so much more powerful when compared with a formula-based approach? After all, when designing an HVAC system, one uses formulas for almost all components of the system; further, the manufacturers provide sufficient information (i.e., pump curves, fan curves, etc.) that one could use, at least theoretically, to predict the performance of the system as a whole and/or of a component of the system. The answer is that formulas are very limited; they only account for a fixed number of variables. For example, it would be significantly challenging to use just the published pump curves to try and operate the pump at its most efficient point; systems effects, sensor accuracy, etc., are latent variables that a typical formula does not account for. Unlike observed/measured variables, latent variables are variables that exist but cannot (easily) be measured and accounted for. Figure 4 shows a potential computational graph for the chiller controls diagram shown in Figure 3. This is where sophisticated AI algorithms (i.e., deep learning) can be relatively more accurate when compared with standard ML algorithms; deep learning or reinforcement learning algorithms tend to be better at accounting for latent variables when making predictions, even if said latent variables are not directly used in the training data. Table 1 shows sample training data that can be used to train an ML algorithm or a deep learning algorithm to predict the energy consumption (expressed in kW) of a chiller.
How does an engineer specify a plant optimization package? Writing a performance spec that reads similar to the chiller water plant optimization solution shall utilize techniques and algorithms that constantly maintain the minimal possible utility cost (electricity, thermal energy and water) for the chiller plant system over a future time horizon, considering the thermal load of the buildings, ambient dry and wet bulb conditions, and electric and thermal utility rate structures are insufficient. The verbiage does not define what minimal possible utility cost really means. Said language gives the provider of the solution the freedom to use low-performing ML algorithms, which may require less time to develop in lieu of high-performing deep learning algorithms. Further, performance verbiage that reads similar to the chilled water plant optimization package shall be capable to determine and optimize chilled water temperature set points, condenser water temperature set points, staging of chillers, water-side economizers and cold storage systems, sequencing and speed control of chilled water and condenser pumps, and sequencing of cooling towers and cooling tower fan speed control are also insufficient in holding the provider of the solution accountable for the performance of the algorithms. The verbiage does not define what capable to determine and optimize means. One could just use an excel formula to comply with this requirement; capable and optimize are not specific enough.
This, in turn, may lead to having an owner paying a significant amount of money for an optimization solution that does not perform as expected and/or desired.
An alternative to the specification verbiage described above is to use more specific language, such as: The chiller water plant optimization solution shall utilize techniques and algorithms that continuously predict the energy performance (i.e., in kW/ton) of the chiller plant system over a future time horizon considering the thermal load of the buildings, ambient dry and wet bulb conditions, and the electric and thermal utility rate structures. Further, the RMSE of each algorithm used to make predictions shall be as follows: 1) No more than 3% for predicting chilled water and condenser water temperatures; 2) No more than 5% when predicting chilled water and condenser water flow; 3) No more than 2% when predicting the kw demand of each chiller; 4) No more than 5% when predicting the energy consumption (in kW/hr) of the tower fans; and 5) No more than 3% when predicting the energy consumption (i.e., in kW/hr) of chilled water and condenser water pumps.
The further strengthens the verbiage described above. One could also include requirements related to 1) continuous (re)training of the algorithms; 2) interval between predictions and communication with the local BAS (i.e., every 5 minutes); and 3) developing new state-of-the-art algorithms at certain time intervals (i.e., every six months).
Even though ASHRAE published Guideline 36, “High Performance Sequences of Operation for HVAC Systems,” sequences of operation, as we have used them for the past decade, will slowly become somewhat obsolete in the next five to 10 years, as said sequences may be used only for gathering data to train algorithms.
As such, engineers will most likely need to adjust and/or improve their knowledge of AI algorithms used for the control of a building system. One will need to have a holistic approach when designing a BAS such that an owner can easily connect a BAS to a cloud-based optimization solution.