Natural Ventilation System Analysisweb.stanford.edu/class/cee243/Projects/Q72012/Madres.pdf · 2 Natural Ventilation System Analysis Introduction The Y2E2 building on Stanfords campus

Stanford University – CEE 243 Query 7

Natural Ventilation System Analysis Y2E2 Building

Brett Madres 6/6/2012

1 Natural Ventilation System Analysis

Contents Introduction .................................................................................................................................................. 2

Analysis Methodology ................................................................................................................................... 3

Assumptions .................................................................................................................................................. 3

Data Manual as a reference guide ................................................................................................................ 4

Current and Updated Versions of the Data Manual ..................................................................................... 5

Current and Updated Versions of the Sequence of Operations ................................................................... 6

Data Points Utilized ....................................................................................................................................... 8

Report SEE-IT data and your analysis of its conformance to functional intent .......................................... 10

Sample Calculations .................................................................................................................................... 12

Night Purge Instance ............................................................................................................................... 12

Smoke Test Instance ............................................................................................................................... 14

Natural Ventilation Instance ................................................................................................................... 15

Aggregate Conformance and Other Summaries ......................................................................................... 18

Lose End Notes ............................................................................................................................................ 20

Points of Concern ........................................................................................................................................ 21

Conclusion ................................................................................................................................................... 21

Guide for Users ........................................................................................................................................... 22

ORID Analysis .............................................................................................................................................. 23

Objective ................................................................................................................................................. 23

Reflective ................................................................................................................................................ 23

Interpretive ............................................................................................................................................. 23

Decisional ................................................................................................................................................ 23

Appendix ..................................................................................................................................................... 24


Introduction The Y2E2 building on Stanford’s campus uses a natural ventilation system as a way to control

temperature and ventilation within the building space. There are two aspects to the natural ventilation

strategy in Y2E2; one is the manual adjustment of personal office windows, and the other is the

automated operation based on the outside and inside conditions of the building. A portion of the

windows in common spaces by the Atria are supplied with electric actuators and controlled by the

Building Management System (BMS). Air will be introduced at each floor through these windows. The

purpose of introducing this air is to take advantage of the free cooling from outside by opening the

automated windows. The buildings thermal mass will cool down from the air being introduced and

reduce the buildings cooling load. Figures 1 and 2 below show examples of the automated windows in

the Y2E2’s atria.

Figure 1: 3rd Floor Natural Ventilation Upper Windows

Figure 2: 3rd Floor Natural Ventilation Window and Actuator


Analysis Methodology It was determined from the start of the analysis that the SEE-IT ID point that was supposed to be

registering the Natural Ventilation Status for AtriumA&B and AtriumC&D pairs was not functioning. But

this only made the task slightly more challenging. It was reasoned that every instance the automated

windows were open represented an instance that the building had gone into natural ventilation mode.

It was later determined that the automated windows open for three primary reasons. The first

and most obvious is for daytime natural ventilation during occupied time. The second is for night purge

during unoccupied time. And the third is the smoke test system which operates every Monday morning

from approximately 5AM to 6AM. The smoke test may not be considered part of the natural ventilation

system necessarily, but none the less it opens the windows and ventilates the building space which is

ultimately what we are concerned with.

From these three window control methods, different scenarios were established to analyze that

are shown in Figure 3 below. Initially it needed to be determined if the occupancy state for the rule

governing the window operation was met or not. If it was not then the data retrieved for that time

stamp was not analyzed for that rule. If the occupancy state was met then the rules conditions were

applied to the data. If all the criteria for that particular rule were not met, then the windows should be

closed by default. If the conditions for that rule did apply then the windows should be open and this was

checked for conformance.

Figure 3: Scenario Analysis Breakdown

Assumptions One key component to this study was identifying what assumptions were made in the analysis.

There were many informational documents and explained directives for how the system should operate

and most of the time these ‘orders’ contradicted each other. When all else failed, assumptions were


made empirically. A list of the main assumptions that were necessary to analyze the data are provided

below:

1. Occupied time is between 6h (6AM) and 19h (7PM) and unoccupied time is all remaining time

during the night and early morning.

2. The BMS code referring to Average OAT was taken as the average of the outside air temperature

taken by the three air handling units.

3. The night purge system deactivates once the space temperature no longer meets the code

requirement that the space temperature is greater than the night purge setpoint. It doesn’t

require the space temperature to drop below a lower night purge disable setpoint temperature.

This assumption is based off of looking at the building management system code.

Data Manual as a reference guide The data manual provided the Figure 4 below which was useful in visualizing the system I was

dealing with as it shows the 3rd floor connectivity between Atrium A & Atrium B. But considering I am in

this building every single day it wasn’t particularly useful to me. The point listings that it shows are not

comprehensive (or close to it) but the points that were used in my analysis that the schematic shows are

circled below on the schematic in red.

Figure 4: Y2E2 Atrium A&B Natural Ventilation Schematic


Figure 5 below is screenshot from the altitude system that shows some values relevant to the

natural ventilation system. The values that were ultimately used from this particular screenshot are

annotated below with their respective SEE-IT ID number next to them as well. Viewing the altitude

system like this was also a good way to make empirical decisions for what Setpoints to use in my

spreadsheet. One problem was that the Setpoints that were getting exported by SEE-IT were not

matching what it showed in the live system. From there I made a judgment call as to which value to use

which is shown in a separate table later in the document.

Figure 5: Y2E2 Atrium A&B Natural Ventilation Altitude Screenshot

Current and Updated Versions of the Data Manual The majority of the updates to the description of the natural ventilation system take place in the

sequence of operations in the following section. However, the data manual has a few updates that could

occur for the sake of clarity:

In the section 1.2 Layout and Scale of the data manual under the Passive Systems section the

data manual says “In hotter months, the building flushes out warmer air and accepts colder air. This is

done when the nighttime air cools to 60F.” This is incorrect. The air needs to be lower than the night

purge enable setpoint but that setpoint would never be as low as 60F. And since the setpoint is

adjustable its safer to just say this in the data manual instead: “In hotter months, the building flushes

out warmer air and accepts colder air. This is done when the nighttime air cools to below the night

purge setpoint.”

Also in the natural ventilation section of the data manual, the second paragraph begins with this

sentence: “The sensors and control points concerning the natural ventilation are illustrated below.” And

8880009

1685


it should read: “The sensors and control points concerning the natural ventilation are illustrated below

in Table 4.” Because Table 4 never gets referenced in the text before it appears.

Current and Updated Versions of the Sequence of Operations Below are quotes directly from the sequence of operations that state the systems operational

intent. After these are stated I will suggest my improvements to the statements based on the results of

the analysis that I have completed. This analysis looked at the building management system code and

the building operator was used as a reference to make determining decisions on how the sequence of

operations and data manual statements can be updated. Main additions to the sequence will be

displayed in bold in the ‘updated’ version and main subtractions will be colored red in the ‘updated’

version.

Natural Ventilation

“Outdoor temperature varies on the roof. BMS shall use an average of the 3 EM&CS sensors, and the BMS sensor. There are four (4) temperature sensors per floor; one per floor per atrium. The operable windows shall be grouped into two temperature control zones per floor. (Atria A&B and Atria C&D) Temperature control within each zone shall operate the associated operable windows. Each pair of atria will have three (3) associated temperature zones (one per floor). For each zone where the average temperature exceeds the enable set point of 74°F (adj) , and the average outdoor temperature is less than the zone temperature (dead band 1°F) the operable windows in the common areas shall also be opened. (Windows in offices are only for smoke control.) If the zone temperature drops below the disable set point of 71° (adj), the windows within that zone shall be closed.” Updated Natural Ventilation

Outdoor temperature varies on the roof. BMS shall use an average of the 3 EM&CS sensors, and the BMS sensor. There are four (4) temperature sensors per floor; one per floor per atrium. The operable windows shall be grouped into two temperature control zones per floor. (Atria A&B and Atria C&D) Temperature control within each zone shall operate the associated operable windows. Each pair of atria will have three (3) associated temperature zones (one per floor). For each zone where the average temperature exceeds the enable set point of 74°F (adj) , and the average outdoor temperature is less than the zone temperature (dead band 1°F) the operable windows in the common areas shall also be opened. (Windows in offices are only for smoke control.) If the zone temperature drops below the disable set point of 71° (adj), the windows within that zone shall be closed. Natural ventilation will only operate during the occupied state of the building. Night Purge

“Daily, during unoccupied hours, the windows shall be opened to pre-cool the space in preparation for the next day. This will allow cool thermal mass storage in the space to offset some of the heat load developed during the day. The night purge space temperature enable set point shall be 68°F (adj). The night purge sequence for each floor will commence only if the average of the two atria space


temperatures (A&B or C&D) is greater than the night purge space temperature set point and the average outdoor air temperature is below the average of the two atria space temperatures. The windows shall remain open until the average atrium space temperature reaches the night purge disable set point of 67.5° (adj). At this point the windows shall close. If the space temperature rises above set point, the windows shall be opened again. This sequence is to be repeated until the space no longer climbs above set point or until the building enters the occupied period.” Updated Night Purge

Daily, during unoccupied hours, the windows shall be opened to pre-cool the space in preparation for the next day. This will allow cool thermal mass storage in the space to offset some of the heat load developed during the day. The night purge space temperature enable set point shall be 68°F (adj). The night purge requires the previous days max average outside air temperature to be greater than 70F. Also the high zone temperature needs to be greater than the average outside air temperature. The night purge sequence for each floor will commence only if the average of the two atria space temperatures (A&B or C&D) is greater than the night purge space temperature set point and the average outdoor air temperature is below the average of the two atria space temperatures. The windows shall remain open until the average atrium space temperature reaches the night purge disable set point of 67.5° (adj). At this point the windows shall close. If the space temperature rises above set point, the windows shall be opened again. This sequence is to be repeated until the space no longer climbs above set point or until the building enters the occupied period. Smoke Test

“The louver dampers at the top of the atrium will be modulating dampers used for building pressurization control, natural ventilation and night flush. The position can be over-ridden and fully opened during the smoke exhaust mode within the building.”

Updated Smoke Test

The windows in the atria and the louver dampers at the top of the atria will be adjustable and used for building pressurization control, natural ventilation and night flush. The position of the windows and louver dampers can be over-ridden and fully opened during the smoke exhaust mode within the building. This mode will be tested prior to every work week on Monday morning from 5:10 AM until 6:10 AM (adj).


Data Points Utilized Table 1 below is a list of all of the SEE-IT data points that were used in the analysis. They

represent points that I exported and eventually manipulated to determine conformance for my system.

The column I added to the table that I thought would have been useful for me is a an indicator value

that lets the user know when the point is no longer functioning. The indicator value would be some

value way out of the realistic range for that specific point. For example, SEE-IT points 1691 and 1692

that refer to the window opening status are bin values with a range of 0 to 1. Therefore as long as the

indicator value for when the points are not functioning is outside that range, like -1, then the user would

know it’s not reliable.

Table 1: List of SEE-IT data points used in the Analysis

Table 2 below is a list of data points that were determined to be not useful from SEE-IT either because

there values were inactive for a long period of time or because they were contradictory to other values

or to what was empirically expected.

Table 2: List of additional relevant but unused SEE-IT data points

Table 3 below is a list of data points that were determined empirically by interpreting the live altitude

system results as well as having conversations with building operators.

Table 3: Empirically Derived Setpoints

Device

Type

Device

Used For

System

Used ForPoint Type Source Unit SEE-IT BMS Identification

Value When Not

Functioning

Outside Air Temp - AHU1 Sensor Degrees F M2:AHU1-AHU1-OutsideAirTemp-(1123)-[F] -100 [F]



Space Temp - Atrium A Sensor Degrees F M2:Atrium A&B-AtriumA (3)-SpaceTemp-(8880009)-[F] -100 [F]

Space Temp - Atrium B Sensor Degrees F M2:Atrium A&B-AtriumB (3)-SpaceTemp-(1685)-[F] -100 [F]

Windows Open A&B Control Bin M2:Atrium A&B-AtriumA&B (3)-WindowsOpen-(1692)-[] -1

Space Temp - Atrium C Sensor Degrees F M2:Atrium C&D-AtriumC (3)-SpaceTemp-(1684)-[F] -100 [F]

Space Temp - Atrium D Sensor Degrees F M2:Atrium C&D-AtriumD (3)-SpaceTemp-(1683)-[F] -100 [F]

Windows Open C&D Control Bin M2:Atrium C&D-AtriumC&D (3)-WindowsOpen-(1691)-[] -1









2nd Floor

1st Floor

Atrium A&B

Atrium C&D

Atrium A&B

Atrium C&D

Atrium C&D

3rd Floor

Natural

Ventilation

&

Night Purge

Atrium A&B

General

Night Purge

Device

Type

Device

Used For

System

Used ForPoint Type Source Unit SEE-IT BMS Identification

Value When Not

Functioning

Nat Vent Status A&B Control Bin M2:Atrium A&B-AtriumA&B1-StatusNatVent-(629)-[] -1

Nat Vent Status A&B Control Bin M2:Atrium A&B-AtriumA&B2-StatusNatVent-(840)-[] -1

Nat Vent Enable Setpoint Sensor Degrees F M2:Atrium A&B-AtriumA&B (3)-DisableSetpointTemp-(1704)-[F] -100 [F]

Nat Vent Disable Setpoint Sensor Degrees F M2:Atrium A&B-AtriumA&B (3)-EnableSetpointTemp-(1705)-[F] -100 [F]

Nat Vent Status C&D Control Bin M2:Atrium C&D-AtriumC&D1-StatusNatVent-(636)-[] -1

Nat Vent Status C&D Control Bin M2:Atrium C&D-AtriumC&D2-StatusNatVent-(834)-[] -1

Nat Vent Enable Setpoint Sensor Degrees F M2:Atrium C&D-AtriumC&D (3)-EnableSetpointTemp-(1699)-[F] -100 [F]

Nat Vent Disable Setpoint Sensor Degrees F M2:Atrium C&D-AtriumC&D (3)-DisableSetpointTemp-(1696)-[F] -100 [F]

Atrium A&B Night Purge Setpoint Sensor Degrees F M2:Atrium A&B-AtriumA&B-Zone Night Purge Setpoint-(1782)-[F] -100 [F]

Atrium C&D Night Purge Setpoint Sensor Degrees F M2:Atrium C&D-AtriumC&D-Zone Night Purge Setpoint-(1780)-[F] -100 [F]Night Purge

Natural

Ventilation

Unknown

3rd Floor

Unknown

3rd Floor

All Floors

Atrium A&B

Atrium C&D


Table 4 is an additional list of the points used in the analysis for this study that details more specific

criteria for the points. A larger version of Table 4 can be found in the Appendix or the live version can be

found in the spreadsheet for the data analysis.

Table 4: Additional PointList Table for Various Criteria

none

71.996 [F]

68 [F]

73.994 [F]

none

none

71.996 [F]

68 [F]

73.994 [F]

none

Night Purge Enable Setpoint

Status of Natural Ventilation

Night Purge Enable Setpoint

Status of Natural Ventilation

Atrium

A&B

Atrium

C&D

Night Purge Disable Setpoint

Night Purge Disable Setpoint

Natural Ventilation Enable Setpoint

Natural Ventilation Enable Setpoint

Natural Ventilation Disable Setpoint

Natural Ventilation Disable Setpoint

ID PointName System Device PointTypeTime

IntervalUnit Source

Correct

ion

Min

normal

value

Max

normal

value

Low normal

operating

range

High

normal

operating

range

Reasons to go outside

normal range

Impact of increased/

decreased value

1123 NVE_EMCS_FSnvoAHU_R1OAT AHU1 AHU1 OutsideAirTemp 1 Degrees F Sensor DivBy10 40 F 80 F 40-50 F 70-80 F severe weather fluctuation higher cooling/heating load



1720 NVE_X282_12nvi106_RMT Atrium A&B AtriumA (1) SpaceTemp 1 Degrees F Sensor 67 F 76 F 67-69 F 74-76 F build. sys. heat/cool malfunction occupant discomfort

8200009 NVE_ECP_300_214nvoI9 Atrium A&B AtriumA (2) SpaceTemp 1 Degrees F Sensor DivBy10 67 F 76 F 67-69 F 74-76 F build. sys. heat/cool malfunction occupant discomfort

8880009 NVE_ECP_300_316nvoI9 Atrium A&B AtriumA (3) SpaceTemp 1 Degrees F Sensor DivBy10 67 F 76 F 67-69 F 74-76 F build. sys. heat/cool malfunction occupant discomfort

626 NVE_X282_12nvo102_RMT Atrium A&B AtriumB (1) SpaceTemp 1 Degrees F Sensor 67 F 76 F 67-69 F 74-76 F build. sys. heat/cool malfunction occupant discomfort



620 NVE_X282_11nvo102_RMT Atrium C&D AtriumC (1) SpaceTemp 1 Degrees F Sensor 67 F 76 F 67-69 F 74-76 F build. sys. heat/cool malfunction occupant discomfort



621 NVE_X282_11nvo103_RMT Atrium C&D AtriumD (1) SpaceTemp 1 Degrees F Sensor 67 F 76 F 67-69 F 74-76 F build. sys. heat/cool malfunction occupant discomfort



1692 NVE_X401_32nvoC_MOW_OP Atrium A&B AtriumA&B (3) WindowsOpen 1 bin Sensor 0 1 0 1 N/A signal of mechanical malfunction

1691 NVE_X401_31nvoC_MOW_OP Atrium C&D AtriumC&D (3) WindowsOpen 1 bin Sensor 0 1 0 1 N/A signal of mechanical malfunction


Report SEE-IT data and your analysis of its conformance to functional

intent The main data set that was used for this analysis spanned from 12:00AM May 3rd, 2012 to

12:00AM May 17th, 2012. Data points were taken once every 5 minutes which totaled 4032 points in

time collected. Figure 6 below shows the full spectrum of 2 weeks of the relevant data that was chosen

to be analyzed. This data was chosen initially based off of observation. I needed to insure that I had

enough points in time where the windows were actually opening so that I would have something to

apply my rules to. Figure 6 has some of the more important points plotted on it that include the outside

air temperature, Atrium C & D space temperatures, the window status bin value, and the night purge

setpoint. No functionality color bands can really be assigned to Figure 6 especially since it was the first

figure I looked at. Functionality color bands will be employed later in the report when specific systems

are being looked individually on smaller scales. There are too many data points on this initial plot to

generalize the system and say that it is functioning in a given way across all these data points.

Figure 6: Full Spectrum of 2 weeks of interesting data

Figure 7 below illustrates the two week time span of data analyzed in a different way and

portrays the instances that the windows are open in purple and the instances that the windows are

closed in red. This offers a different way to diagnose an initial observable problem in our data set that

could not be easily noticed in Figure 6. Figure 7 shows (and the data supports) that the windows never

open during the occupied time period of the day which is the solid region in red between the two

superimposed yellow lines. The region below and above the yellow line represent unoccupied time

states. The purple on the plot above the top yellow line is due to the night purge system and the purple

on the plot below the bottom yellow line is due to the smoke test system. This figure was also my first

glimpse at the fact that the AtriumA&B pair and AtriumC&D pair are dependent on each other with

respect to the night purge system due to the fact that the two plots are identical to each other meaning

Fluctuations in the red

line represent window

activity.

1 = closed.

0 = open.


the Atrium pairs always have their windows open at the same time (at least in the unoccupied state).

Figure 7 gets an initial reed functionality band for unintended behavior of the natural ventilation never

being active during the day. This will be proven to be unintended in an example calculation later in the

report. The building operator at this point should note the lack of functionality in the natural ventilation

system and either verifies the code or check mechanical features of the system or something else that

they would know in their expertise.

Figure 7: Window Open Status as a function of time of day

In my opinion Figure 6 has a weakness in the difficulty to digest all the information on it quickly.

But then again it was an initial large scope graph to try to put many data points on it and establish if it

was a worthwhile time series to look into further. So for what it was used for it still had value. Figure 7

was much better in that it only plotted one point. When you only plot a few number of points on the

graph or you only look at a small time span it is much easier to make a quick interpretation of what is

occurring. Figure 7’s strength lies in the power of a single message being delivered without other

information to clutter it.

Superimposed yellow lines illustrating that in

occupied time state the windows never open.


As a guideline for status clarification, it goes back to the strength of Figure 7. You need to know

ahead of time what you are trying to prove or disprove and only put the necessary information into the

graphical display. The fewer points, the better. You should be putting to the test only the relevant data

points. Also annotation of the plots is crucial for pointing out either the conformance or

nonconformance on the plot to make it believable to the viewer and to yourself. Make the key features

stand out with annotations and put a functional color band around the plot. Keeping it simple in a small

time frame makes it easier to prove a distinct point as well about the data you are looking at and will

focus the viewer on a smaller more selective set of data appoints. So that time range should be chosen

carefully as well when trying to do status classification.

Sample Calculations The following sample calculations and demonstrations of specific rules will be done from the same set of

data plotted in Figure 6 except we will zoom in on specific regions and only look at one rule at a time to

observe functionality.

Night Purge Instance In this section one specific instance for each rule that governs window operation will be looked at and

plotted and a conclusion about the conformance for that instance will be made.

Figure 8 below is an example of a day span where the night purge is operating correctly and given a

‘green light’ for the system. Once the building reaches unoccupied hours for that day, all of the other

rule criteria was met and therefore the windows opened promptly upon reaching the unoccupied state.

Figure 8: Properly functioning night purge system:

Time = 19:00h (unoccupied

state); windows open

Space Temp reaches Disable

Setpoint; windows close


Operationalized Rule:

Tests if Max(OAT) > 70F, High Zone Temp > AvgOAT, if building is unoccupied, and if the OAT < Night

Purge Setpoint and Avg Zone Temp > Night Purge Setpoint then the windows should be open.*

*because this rule is composed of values that have been averaged and setpoints that have been

empirically derived, the points in the above statement don’t have specific SEE-IT ID’s to reference but in

the spreadsheet you can see all the exact points and how the averages were taken, etc.

Example Excel Code:

=IF(AND(max(OAT)>70,HighZoneTemp>AvgOAT,unoccupied=“yes”,OAT<NightPurgeSetpoint,AvgZoneTe

mp>NightPurgeSetpoint), ….. check windows**

**for full excel format and authentic code please reference the spreadsheet

Night Purge Sample Data:

Conformance for overall Night Purge System:

Strictly when the night purge conditions were met there was 84.43% (179 correct / 212 total)

conformance. When the entire unoccupied state was tested there was an 80.41% (1486 correct / 1848

total) conformance.

Building operator actions:

No actions need to be taken under these night purge conditions as it is functioning properly. However,

there is still a setpoint issue in later night purge events where it is unclear what temperature the night

purge should be disabling do to multiple instances of night purge stop times at various temperatures. I


would recommend double checking the disable setpoint temp for night purge. From my vantage point

its difficult to be sure that the night purge is always disabling when it should be .

Smoke Test Instance Figure 9 below is an example of a properly functioning smoke test system sample from a Monday

morning during my time interval. The BMS accurately opens the window at approximately 5:10AM and

the window remains open for the duration of the hour long smoke test until it closes at 6:10AM. This

system was given a ‘green light’ for its correct functionality.

Figure 9: Properly Functioning Smoke Test System


If windows are open on Monday morning from approximately 5:10 AM to 6:10 AM, then it is conforming

to the Smoke Test System.

Sample excel code:

=IF(AND(Day=“Monday”,AND(Time>=5:10,Time<=6:10)), …….. check windows*

*This code is just a visual representation , simplified for readers understanding

Smoke Test Sample Data:

Time = 5:10h;

windows open

Time = 6:10h;

windows close


Smoke Test Conformance:

When the smoke test conditions were met (which is the only reasonable way to test conformance for

something like this) the windows conformed 100% (26 correct / 26 total) of the time.

Building Operator Actions:

No actions should be taken for the building operator based on this example unless he wanted to shift

the smoke test operating hours to a warmer time of day or to a shorter time period to avoid energy

losses during a very cold part of the morning.

Natural Ventilation Instance Figure 10 below is a demonstration of the natural ventilation not functioning properly. The natural

ventilation system in this instance was given a ‘red light’ for its lack of functionality according to the

rules. The problem here as the notations will point out on the figure is that the AvgZoneTemp does

exceed 74F and the Avg OAT is less than the Avg Zone Temp A&B as defined in the rules below and the

time state is occupied however the windows remain closed. This is a nonconforming example.

Figure 10: Malfunctioning Natural Ventilation system:



Tests if Avg Zone Temp A&B > 74F and if Avg OAT < Avg Zone TempA&B and in occupied hours then the

windows should be open in A&B.*

Or

Tests if Avg Zone Temp C&D > 74F and if Avg OAT < Avg Zone TempC&D and in occupied hours then the

windows should be open in C&D.*

*Again here since averages are used , you cant track the values used in the rule to direct SEE-IT ID points

and rather they are averaged values but the points can be tracked in the spreadsheet.

Excel Code:

Excel Code: =IF(AND(AvgZoneTempA&B>74,AvgOAT<AvgZoneTempA&B,Occupied=“yes”), ….. check

windows**

Or

Excel Code: =IF(AND(AvgZoneTempC&D>74,AvgOAT<AvgZoneTempC&D,Occupied=“yes”), ….. check

windows**

Avg Oat (green line) < Avg Zone Temp A&B …..

windows should be open

AvgZoneTempA&B > 74

(red+blue lie)

Windows are closed

(maroon line)


**for full excel format and authentic code please reference the spreadsheet

Natural Ventilation Sample Data:

Natural Ventilation Conformance:

When the natural ventilation conditions were met, Atrium A&B conformed 0% (0 correct / 32 total)

times and Atrium C&D conformed 0% (0 correct / 216 total) times. When the state was occupied the

natural ventilation conformance for Atrium A&B was 98.26% (2146correct / 2184 total) conformance

and for Atrium C&D was 89.84% (1962 correct / 2184 total).

Building operator actions:

The building operator must get to the bottom of the lack of conformance when the natural ventilation

conditions are met. It may be an error In the code somewhere that has led it to stop functioning. That

would be the first and probably easiest place to start looking for problems to solve this. This example is

proof that the system is not working and in fact that are at least a few other instances over the two

week span where similar instances are taking place. This leads to a loss in potential savings in energy

over time so it is worth investigating sooner rather than later.

Summarizing thoughts on sample size:

Interestingly enough, even though each of these instances for the three systems only represents one

look in time, they are representative of the respective system over the entire time span. This is because


the natural ventilation system never operates properly, because it never operates at all. So it would

always receive a ‘red light’. The smoke test system always operates properly, so it would always receive

a ‘green light’. And the night purge consistently works properly and would almost always receive ‘green

lights’ with a ‘yellow light’ or two mixed in.

Aggregate Conformance and Other Summaries In Table 5 you can now see the aggregate composite rule analysis. This takes into account the

natural ventilation, the night purge, and the smoke test all at once. So what is happening in the first two

columns is only the times when one of the three rule conditions are met will the conformance be tested

against. That is why there is a smaller sample size. But the two columns on the right are testing against

every single data point (4032) including times when none of the rules apply. So it’s just two separate

ways to look at conformance selectively or non selectively.

Table 5: Aggregate conformance results from all three rules combined

As noted earlier the Smoke test opens the windows for an hour which is excessive considering

that the outside air temperature is quite low at this time and having the windows open for an hour

significantly cools off the Atrium spaces as depicted in Figure 11 below.

Figure 11: Impact of Smoke Test on Atrium D Space Temperature

Atrium A&B Atrium C&D Atrium A&B Atrium C&D

When conditions are met When conditions are met all time states all time states

Conformance % = 75.93% 45.15% 90.67% 86.11%

Correct = 205 205 3656 3472

Incorrect = 65 249 376 560

Total Count = 270 454 4032 4032

Composite Rule Analysis

Atrium D Space Temp shows significant

decrease during Window Smoke Test


Table 6 below is depicting the sum conformance count when night purge conditions are met

with respect to the hour of day. There is no real trend here that can be traced back and said there is

causation between nonconformance and time of day for the night purge system in my opinion.

Table 6: Night purge conformance totals with respect to hour of the day

Count of Are

windows conforming

to this rule when

N.P. conditions are Column Labels

Row Labels N/A no yes Grand Total

0 60 108 168

1 49 119 168

2 11 157 168

3 168 168

4 168 168

5 18 150 168

6 168 168

7 168 168

8 168 168

9 168 168

10 168 168

11 168 168

12 168 168

13 168 168

14 168 168

15 168 168

16 168 168

17 168 168

18 168 168

19 28 140 168

20 40 128 168

21 32 136 168

22 56 112 168

23 68 100 168

Grand Total 2184 362 1486 4032


Lose End Notes Over the past year, the natural ventilation was only operating for approximately 66 hours of the

possible approximate 4800 hours of occupied time which translates to natural ventilation being used

1.3% of the time the rule is active in the occupied time state.

In my 2 week time span the natural ventilation was never used (0% operation) whereas my rules

for the operation of the natural ventilation system say there should be about 1.5% use for Atrium A&B

and about 9.9% use for Atrium C&D. The Atrium pairs are analyzed separately for the natural ventilation

system. The discrepancy between Atrium A&B and Atrium C&D relies solely on the difference in Average

Atrium temperature which causes Atrium C&D to meet the natural ventilation requirements more often

than Atrium A&B. This temperature difference could just be due to the difference in orientation of the

atria.

The reason why the natural ventilation never gets utilized is unclear at this time but it most

likely is widely underutilized over the course of a year based on my yearlong data export mentioned

above. There may be some Setpoint differences or something just unknowable to myself that is taking

place. Currently my code is evaluating the data just as the BMS system code is (minus the potential of a

rounding error to the integers place which is not enough to yield 0% operational results).

The night purge system was the dominating mechanism for opening the windows in my analysis.

But in the night purge system the Atrium pairs are looked at separately but if either Atrium pair dictates

the windows should be open, it will actually open the windows across the entire floor and not just the

Atrium pair, and this was captured in my analysis and my rule making.


Points of Concern Rounding errors from decimals into integers in the BMS may lead to some conformance issues when

looking at subtle temperature differences.

StatusNatVent points in SEE-IT worked from Aug – Oct 2009 then produced no readings up until about

the middle of May 2012, but these reading are clearly faulty with the StatusNatVent registering as

operable for entire weeks straight which Is nonsense.

The StatusNatVent points in SEE-IT are as follows:

M2:Atrium A&B-AtriumA&B1-StatusNatVent-(629)-[]

M2:Atrium A&B-AtriumA&B2-StatusNatVent-(840)-[]

M2:Atrium C&D-AtriumC&D1-StatusNatVent-(636)-[]

M2:Atrium C&D-AtriumC&D2-StatusNatVent-(834)-[]

It is also unclear why there are not six of these points in SEE-IT; one for each Atrium pair per floor for all

3 floors. So it isn’t decipherable what floor each point is in reference to.

In Altitude/Opendium when you click the sequence of operations button for the natural ventilation

system it references the 2007 version of the Sequence of Operations even though updates have been

made to the sequence in 2010 and so now they are following an updated sequence. So basically the old

sequence should stop being referenced to in the live version of Altitude/Opendium to avoid confusion.

Further research suggestions would be to first verify the accuracy of the rules created in the

spreadsheet. Then I would suggest looking at longer time spans of data, for example multiple months to

a year time spans. I would also suggest branching out from looking at just a single floor, like I did, to

looking at the whole building which includes all 3 floors for analysis. Perhaps the natural ventilation

system is working on the 1st and 2nd floors but just not on the 3rd and something can be recognized and

the problem fixed perhaps.

Conclusion My concluding remarks on the natural ventilation system are that it is fulfilling its purpose to

save the building energy. It is operating pretty close to its functional intent based on the operations and

code written for the system. The night purge operates on the nights that it should even if it doesn’t

always start or stop at the exact anticipated time it still is filling the space with cool air that cools the

thermal mass and saves energy. The night purge is the dominating mechanism in the natural ventilation

system and it is operating quite well based on conformance and energy saving potential. The smoke test

is also functioning properly and no adjustment needs to be (or is allowed to be) made here. The natural

ventilation was shown to not operate very much over the course of an entire year even if it was working,

so it may not lead to much additional energy savings to fix it but it should still definitely be looked at by

coders/building operators to double check its governance by the system.


Guide for Users The diagnostic process that I went through to perform my analysis can be replicated for any

system. A buildings data manual and live data system (equivalent to Altitude) can be used to understand

your system and get a starting point for what the names of the points or mechanical features you want

to retrieve data for. After that you can look at the sequence of operations for the system you desire to

analyze. The sequence should also offer additional critical data points worth pursuing that are part of

your system that the data manual did not offer. From there you can access the data from the Building

Management System (equivalent to SEE-IT) and plot some of the points you are interested in over a

large time span. A large time span can be a good starting point because from there you can zoom in as

you please to more specific areas that may be more useful to the system you are trying to analyze. After

some eyeball observations of the data you need to write conformance rules based on the sequence of

operations and you should be as thorough as you possibly can be in trying to conform to the lingo and

the guidance that you have available. From there you can test your data against your rule for

conformance. If you want to try to use your time the wisest and save the building the most energy you

should focus on recurring trends of nonconformance so that when you solve the problem they can fix

many areas of nonconformance. Or you can choose to focus on areas of nonconformance that have

significant errors associated with them leading to larger energy losses than just a typical

nonconformance case. Choose your battles wisely in terms of which problems you want to solve if there

are many options. It will take some serious iterating as well so don’t get discouraged.


ORID Analysis

Objective I saw that to get a comprehensive understanding of a single system, it will almost certainly require

looking at multiple rules of governance and applying these rules in tandem to determine functional

intent. Also that systems that a you might not normally expect to have an impact on your system may

indeed affect the ultimate purpose. I would not have expected a standard smoke test to impact the

natural ventilation system but it will actually affect the purpose of the system and lead to excessive

cooling of the building space. Another fact I saw was that it might take 20-30 unique SEE-IT data points

to analyze a system correctly and that managing this and organizing it within your spreadsheet is crucial

to not get lost in your own work.

Reflective At the end of the day I was definitely surprised to see the natural ventilation system not operating

during the time span I choose to analyze. That’s kind of unfortunate because it would have been nice to

understand more about it but I realized too late that it was actually the night purge responsible for all

the openings when originally I thought it might not have been that way. I was encouraged that the one

time I had to deal with missing data early in the quarter was the only time that came up. Because I

learned how to deal with the issue and added that to my skill set but then I never had to deal with it

again! It is discouraging to apply the BMS code to my exported SEE-IT data and not get a higher

conformance rate. Nothing operates perfectly but I would have liked to see the natural ventilation

system turn on in one of the instances where it clearly should have during my analyzed time span.

Interpretive For me this quarter was about dealing with ambiguity and real data and real world scenarios. It wasn’t

about structured problem sets for a change. I think looking at active intelligent building systems was

really exciting, especially getting to see all of the data that Y2E2 collects every day. I found that

occasionally problems in the systems operations can be hard to find. And once you find them, they are

even harder to solve. It is extremely important to be under the right ‘guidance’ when performing these

analyses. By guidance I am referring to the correct and up to date manuals and data points. These will

help so much. I also learned that these multiple pieces of ‘guidance’ are usually contradictory in some

way and are not up to date. Relying on human beings and personal intuition still seems like a perfectly

good method to be guiding factors for analysis.

Decisional Next steps need to include analysis on a longer time span of multiple months and also across all three

floors and not just the 3rd floor like I did. This shouldn’t be very difficult to do considering the

spreadsheet is already assembled and the same rules will apply for the first two floors that apply for the

third floor. Again for the sake of being comprehensive this is really important. There may just be

something unique to the third floor that has an issue. Also the building operator needs to look at the

natural ventilation code in the BMS to make sure it is satisfactory and determine why it is not operating

at the moment. Also correcting points in SEE-IT like the all-important StatusNatVent points would make


the job a lot easier on anyone trying to continue looking at this system.

Appendix


ID

Po

intN

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In

terv

al

Un

itS

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ion

Min

no

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valu

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Max

no

rmal

valu

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Lo

w n

orm

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op

era

ting

ran

ge

Hig

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no

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op

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ting

ran

ge

Reaso

ns to

go

ou

tsid

e

no

rmal ra

ng

e

Im

pact o

f incre

ased

/

decre

ased

valu

e

1123N

VE_EM

CS_FSnvo

AH

U_R

1OA

TA

HU

1A

HU

1O

utsideAirTem

p1

Degrees F

Senso

rD

ivBy10

40 F80 F

40-50 F70-80 F

severe weather fluctuatio

nhigher co

oling/h

eating load

1124N

VE_EM

CS_FSnvo

AH

U_R

2OA

TA

HU

2A

HU

2O

utsideAirTem

p1

Degrees F

Senso

rD

ivBy10

40 F80 F

40-50 F70-80 F


nhigher co

oling/h

eating load

1125N

VE_EM

CS_FSnvo

AH

U_R

3OA

TA

HU

3A

HU

3O

utsideAirTem

p1

Degrees F

Senso

rD

ivBy10

40 F80 F

40-50 F70-80 F


nhigher co

oling/h

eating load

1720N

VE_X

282_12nvi106_R

MT

Atrium

A&

BA

triumA

(1)SpaceTem

p1

Degrees F

Senso

r67 F

76 F67-69 F

74-76 Fbuild. sys. heat/co

ol m

alfunction

occupant disco

mfo

rt

8200009N

VE_EC

P_300_2

14nvoI9

Atrium

A&

BA

triumA

(2)SpaceTem

p1

Degrees F

Senso

rD

ivBy10

67 F76 F

67-69 F74-76 F

build. sys. heat/coo

l malfunctio

no

ccupant discom

fort

8880009N

VE_EC

P_300_3

16nvoI9

Atrium

A&

BA

triumA

(3)SpaceTem

p1

Degrees F

Senso

rD

ivBy10

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

626N

VE_X

282_12nvo

102_RM

TA

trium A

&B

Atrium

B (1)

SpaceTemp

1D

egrees FSen

sor

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

837N

VE_X

282_22nvo

202_RM

TA

trium A

&B

Atrium

B (2)

SpaceTemp

1D

egrees FSen

sor

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

1685N

VE_X

401_32nvo

302_RM

TA

trium A

&B

Atrium

B (3)

SpaceTemp

1D

egrees FSen

sor

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

620N

VE_X

282_11nvo

102_RM

TA

trium C

&D

Atrium

C (1)

SpaceTemp

1D

egrees FSen

sor

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

831N

VE_X

282_21nvo

203_RM

TA

trium C

&D

Atrium

C (2)

SpaceTemp

1D

egrees FSen

sor

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

1684N

VE_X

401_31nvo

303_RM

TA

trium C

&D

Atrium

C (3)

SpaceTemp

1D

egrees FSen

sor

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

621N

VE_X

282_11nvo

103_RM

TA

trium C

&D

Atrium

D (1)

SpaceTemp

1D

egrees FSen

sor

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

830N

VE_X

282_21nvo

202_RM

TA

trium C

&D

Atrium

D (2)

SpaceTemp

1D

egrees FSen

sor

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

1683N

VE_X

401_31nvo

302_RM

TA

trium C

&D

Atrium

D (3)

SpaceTemp

1D

egrees FSen

sor

67 F76 F

67-69 F74-76 F


l malfunctio

no

ccupant discom

fort

1692N

VE_X

401_32nvo

C_M

OW

_OP

Atrium

A&

BA

triumA

&B

(3)W

indow

sOpen

1bin

Senso

r0

10

1N

/Asignal o

f mechanical m

alfunction

1691N

VE_X

401_31nvo

C_M

OW

_OP

Atrium

C&

DA

triumC

&D

(3)W

indow

sOpen

1bin

Senso

r0

10

1N

/Asignal o

f mechanical m

alfunction

Documents

Natural Ventilation System Analysisweb.stanford.edu/class/cee243/Projects/Q72012/Madres.pdf · 2 Natural Ventilation System Analysis Introduction The Y2E2 building on Stanfords campus