The building that will be focused on in this study is the United World College Campus in Singapore. It is a 70,000 sq.meter International School in which construction was completed in June 2011 and students first utilized the facility in September of that year. It is a certified Greemark Platinum building which means it is at the top of building ratings in terms of energy efficiency and sustainability.
The facility is extraordinary from a data analysis standpoint as it has a few extra sensors meansuring key energy information and that it is trending and storing much of the data generated by the building controls system. This is much different than a conventional commercial building which hardly ever stores any of the data from its controls systems. The main reason behind this is that operations staff find that too much raw data is unuseful and confusing.
UWC is different in that they would like to store and manage as much data as possible and look to researchers to help then get value from this repository. We will focus on UWC’s cooling system as it is approximately 65% of all the energy consumed on campus as year-round cooling and dehumidification are required in Singapore’s climate. UWC spends approximately SGD $75,000 (40,000 Euros)/month on cooling energy consumption!
Use case:
Energy Optimization and Analytics for Campus-Scale Cooling Systems
| Example Observations | Ambient variables: zone temperature, humidity, CO₂, state of openings (doors/windows) Outdoor weather conditions: temperature, humidity, wind speed/direction, solar irradiance HVAC state: AHU supply air temperature, chilled water valve position, fan speed, compressor status, active power from substation meter, time/day |
| Example Actions | AHU supply air temperature (°C) Zone temperature setpoint (°C) Fan speed (0–100%) Chilled water valve opening (0–100%) On/off scheduling programs (boolean) |
| Example Reward | rt=−Et(kWh)−λ⋅max(0,∣Tzonet−Tcomfort∣−Δ)−μ⋅max(0,CO2t−1000 ppm) |