IS428 2016-17 Term1 Assign3 Lee Wai Tong Arnold

Challenges and strategy to overcome

1. One problem when doing this assignment was the lack of knowledge about buildings and its ventilation systems. As such, it was difficult to understand how HVAC and VAV Systems operate and their terminology. To overcome this, extra time had to be spent researching and understanding how do these systems work and how they correlate with one another. The improved background understanding offered better appreciation of the data given.
2. Another issue was prox-id. All employees issued card has a prox-id which ends with a number. Eg. 001. As such, when employees lose their cards and get a replacement, they will be issued with another card numbered 002. At the start, there were prox-id with same alphabets but different ending numbers. On closer investigation, the increment in numbers suggests that those were replacement cards but by the same employee. As such, I created another field called userid and removed the trailing numbers, leaving only the alphabets. This helps to identify the employee despite the different prox-id.

Answering questions

1. What are the typical patterns in the prox card data? What does a typical day look like for GAStech employees?

1. Most employees report to work from 7am onwards. Data reveals that during the 7th hour, employee activity is the highest.
2. Lunch time at work is around 12pm onwards. For most employees, there is an increase in activity on the 12th hour.
3. Office hours is until 5pm. For most employees, the last peak in activity is at 5pm after which there is no activity. This suggests that the employees are no longer in the office.
4. Most departments only work until 5pm and go home. These departments are Admin, Executive, HR and Security. Most of these employees do not have any recorded activity after 5pm.
5. Other departments like Engineering, Information Technology and Facilities tend to have longer operating hours as there are recorded activity from 7am until 11pm.
6. On closer look, some employees that are detected in the office after office hours only have recorded activity from 4pm onwards. This suggests that they probably work the night shift and hence get off work later.
7. Employees that report to work at 7am tend to get off work before 8pm. This suggests that employees usually do not need to work overtime.

2. Describe up to ten of the most interesting patterns that appear in the building data. Describe what is notable about the pattern and explain its possible significance.

1. Lights power and equipment power in certain floor zones share similar patterns. Example of such places are the offices on the 2nd floor. A possible reasoning is that an employee has entered the zone to do something. This suggests that the employee switched on the lights and use the equipment which explains to similar pattern in light and equipment power consumption.
2. Some floor zones have constant usage of light power. An explanation for this is that these areas are common areas and thus the lights are always kept on for safety reasons.
3. Another pattern that is observed is that lights and equipment power is various zones usually increase sharply from 6h and drops close to 0 after 17h. This probably maps the employees’ behavior in the office whereby the arrive into work in the morning and leave after office hours.
4. Water heater gas rates is usually low during the weekends when there are no employees. However, during the weekdays, it is observed that the gas rate usually peaks at 12h, which coincides with the lunch hour. It is likely that employees are consuming more hot water during lunch which results in the water heater having to heat more water and thus the higher gas rate.
5. Reheat damper position. It usually follows the employee activity patterns whereby there are higher recordings during office hours. This is likely due to damper position being used to regulate ventilation in the building and hence are more active during that time.
6. Reheat damper position & supply inlet mass flow. From observation, these 2 readings follow very similar patterns. This suggests that they correlate with one another very strongly as when the damper position is opened more widely, it increases the inlet mass flow rate by the same constant.
7. Total electric demand power. It follows a similar pattern whereby it goes up during office hours. During the day, it has 3 peaks during 8h, 12h and 17h. This coincides with employees arriving for work, going for lunch and leaving the office. The employees use more power during these hours. It also follows a similar pattern with HVAC electric consumption. It can mean that the increase electric consumption by HVAC significantly contribute to the increase in overall electric consumption.

3. Describe up to ten notable anomalies or unusual events you see in the data. Prioritize those issues that are most likely to represent a danger or a serious issue for building operations.

1. On 11 june after 10am, hazium levels started to shoot upwards. Most levels reached the peak at 18 hour before starting to decline. Whereas, hazium level on level 3 continued to remain high until 21 hour.
2. On 7 and 8 june, the building temperature was much higher than usual. The higher temperature may make it uncomfortable for employees who are working in the building.
3. It is observed that fan power on 7 and 8 june is not functioning well. The fan power on both days are observed to be lower than usual. This means that air might not be ventilated inside the building, posing a risk to employees with respiratory problems.
4. Co2 concentration in the building on 7 and 8 was also observed to be higher on both days. This may be due to the poor ventilation as highlighted above. Higher co2 concentration may pose a risk to employees with respiratory problem.
5. During the weekend of June 11 and 12, it is observed that HVAC demand power was high. This suggests that the HVAC systems were left on over the weekend. The power consumption was also much higher than normal office consumption. This suggests that there was some testing going on with the HVAC systems over the weekend, resulting in the high consumption of HVAC power.
6. Electric consumption. During the weekend when most employees are not in office, it makes sense that electric consumption is low. However, on the weekend of June 11 and 12, it is observed that electricity consumption over that weekend remains high.
7. Reheat coil power. During the weekend of june 11 and 12, reheat coil power was high and not in its usual form. This means that more heat is generated by the system and would result in warmer air to be produced in the building. This may explain why HVAC power consumption was high over the same period because it was trying to cool the building down.
8. Reheat damper position. During the weekend of june 11 and 12, the reheat damper position was also left open. Because of that, heated air entered the building and cause the HVAC systems to work to bring down the building temperature.

4. Describe up to five observed relationships between the proximity card data and building data elements. If you find a causal relationship (for example, a building event or condition leading to personnel behavior changes or personnel activity leading to building operations changes), describe your discovered cause and effect, the evidence you found to support it, and your level of confidence in your assessment of the relationship.

Tools used

• Tableau
• Microsoft Excel
• Java code for extracting and printing data