Living lab

ADSC’s office in Fusionoplis has become a living lab! We worked together with our colleagues from Interactive Digital Media (IDM) subprogram at ADSC to deploy the necessary sensors and integrate many of the enabling technologies developed at ADSC to accomplish this goal.In particular, we are using a variety of sensors (current / voltage / PIR / light / air pressure / temperature / humidity / CO2 / microphone / camera / radio spectrum) across the ADSC premises to monitor the office space, people, appliances, and their energy consumptions. The data gathered by these sensors are centrally analysed and processed. Users of the system, which include ADSC staffs as well as visitors, are able to access the data live (e.g., how much power am I consuming now?) as well as for trending analysis and future planning purposes (e.g., how the room temperature varies over time and space). One week power consumption at ADSC
Livng lab sensorsApplications that are enabled by this living lab include:

  • Personalized power-consumption assistant, with features such as detailed energy consumption breakdown and automatic PC monitors switch-off. The goal is to engage the real users.
  • Fine-grained public area monitoring and control, with features such as automatic adjustment of air pump valves, lighting, or other appliances’ status based on recognized user activity.
  • User-friendly demand response decisions (e.g. shifting the air conditioner / refrigerator / water cooler’s load to avoid peak hours without the users even noticing that!).

Our Approaches

In the process of setting up this living lab, we have developed enabling technologies to achieve more efficient and accurate electricity consumption monitoring, auditing, and learning. These include:

  • Group/ Individual/ Activity level energy consumption breakdown.
  • Quantifying achievable energy saving by utilizing occupancy and environment information from sensors.
  • Learning and identifying important electrical events (e.g. Turning ON/OFF Microwave) and real-time tracking of usage information of appliances of interest.
  • Extracting high-level information of people’s action or contextual information based on raw power consumption data. A dual problem (for privacy protection) is to develop methods that can process the raw consumption data so as to (selectively) hide some high-level information.

We are also investigating technologies that can make occupancy sensing more efficient and accurate, for example, by leveraging measurement results from different sensors, or through fine-grained analysis of RF signals.