1.Challenge As an building energy saving solution architect, I had to persuade and demonstrate why the operators should take  an action following my data analytic result.

2.Selection Rather than explaining A to Z about how well the building’s HVAC system operated, I tried to use Python for not  just analyzing the problem but also finding solutions.

3.Application First, visualize using Python what happened on whole building for 24 hours. When the HVAC system turned  on ,how much the damper opened and what was the indoor air quality was when system operated, which was  like demonstrating system performance and system operation efficiency clearly for the operators in order to find  out the problem by themselves.

4.Insight In many cases, just one insightful graph or picture tells us everything what the author would like to explain. In this  case, the operators realize easily why they need to change system turn on/off time and setting point just by  watching the graph I prepared which was another good lesson I learned from the real working situation.

5.Approach Next time I will spend less time in finding the problem, it is better to communicate with the operators with few  slides and initial analytics in early stage. I should not had to prepare the perfect solutions.

24hours Free cooling analysis report(2019.4.18)

Enthalpy(kj/kg)

Outdoor temp and

damper open rate

Outdoor enthalpy Indoor enthalpy Return temperature Damper open rate

Damper opened 100% when outdoor  enthalpy was higher than indoor  enthalpy which can cause energy loss

Visualization Image sample for showing the problem


1.Challenge As an building energy saving solution architect , I had to persuade and demonstrate why the operators should take an action following my data analytic result . 2.Selection Rather than explaining A to Z about how well the building ’s HVAC system operated , I tried to use Python for not just analyzing the problem but also finding solutions . 3.Application First , visualize using Python what happened on whole building for 24 hours . When the HVAC system turned on , how much the damper opened and what was the indoor air quality was when system operated , which was like demonstrating system performance and system operation efficiency clearly for the operators in order to find out the problem by themselves . 4.Insight In many cases , just one insightful graph or picture tells us everything what the author would like to explain . In this case , the operators realize easily why they need to change system turn on / off time and setting point just by watching the graph I prepared which was another good lesson I learned from the real working situation . 5.Approach Next time I will spend less time in finding the problem , it is better to communicate with the operators with few slides and initial analytics in early stage . I should not had to prepare the perfect solutions . 24hours Free cooling analysis report(2019.4.18 ) Enthalpy(kj / kg ) Outdoor temp and damper open rate Outdoor enthalpy Indoor enthalpy Return temperature Damper open rate Damper opened 100 % when outdoor enthalpy was higher than indoor enthalpy which can cause energy loss Visualization Image sample for showing the problem