FOCUS: Energy Efficiency, Smartphone Lidar Scanning

OVERVIEW: Q Sense is a thermal-LiDAR sensor used in a diagnostic capacity to improve energy efficiency and occupant thermal comfort in buildings. Q Sense generates detailed thermal digital twins in minutes, accurately models heat transfer and uncovers opportunities to improve building performance.

Q Sense combines two key components: an iPhone and our thermal module hardware. During scanning, it fuses geometry information from the iPhone's cameras, motion sensor and LiDAR with thermal data from our hardware. A typical dataset contains 12 million points and takes about 10 minutes to collect. Q Sense is the first way to practically generate 3D thermal data in buildings which is critical to measuring heat transfer. In a typical building, half of heat transfer is thermal radiation which is dependent on both surface temperatures and geometry.

There are three steps to our technology: data collection, heat transfer/geometry analysis and use-case specific machine learning models. Everything is fully automated—reducing the cost of advanced thermal analysis. These layers translate the raw data into actionable insights in our Analyze → Optimize → Retrofit framework.

_Analyze_: Automatically analyze the building to measure heat transfer, quantify thermal comfort, calculate U-Values and reveal energy leaks. Critically, it can quantify the impact of leaks on both energy consumption and occupant comfort.

_Optimize_: Recalculate the existing HVAC setpoints based on this information. This fixes the causes of occupant discomfort, avoids hot/cold calls for facilities managers, and usually saves energy (averaging 8.7%).

_Retrofit_: Generate project options to decarbonize a building based on real data. Each option has quantified performance metrics (e.g. energy savings, ROI, comfort improvement). For example, it can find an envelope issue, design the size, shape and type of insulation to address it, and then compare that option to other retrofits like replacing the glazing. This informs decision-making, helps justify the expenditure, unlock financing and drive adoption.

There is an existing market for thermal and energy analysis of buildings. Currently, it is bifurcated between technical sensors (globe thermometers, thermal cameras etc) and services businesses that provide analysis using these sensors (AECOM, Jacobs Engineering, etc). Q Sense integrates these approaches by using better sensors and automating analysis through software. It is a diagnostic tool used across a portfolio of buildings by either internal facilities teams or external services businesses.

We offer Q Sense using a hardware-as-a-service (HaaS) approach that includes everything (i.e. an iPhone Pro Max, thermal hardware, software, and algorithms) for $499/month. 

Radiant heat transfer occurs between all surfaces and objects, and is entirely separate from the air temperature. In a typical indoor environment, it accounts for approximately half of total heat transfer for a person and half of their thermal comfort. The impact of radiant heat transfer has also been systemically underestimated due to inaccurate measurement techniques (black globe thermometers), as we previously published in academia (see https://www.nature.com/articles/s41598-020-59441-1). Radiant heat transfer is challenging to measure because requires measuring both (1) surface temperatures for all surfaces, and (2) the geometry of surrounding surfaces to calculate view factors Q Sense is the first practical way to generate 3D thermal data sets in buildings and fully characterize heat transfer.

Measuring all heat transfer allows Q Sense to model heat flows throughout the building. For example, it can use surface temperatures to measure conduction through a wall and calculate insulation (R) values. Q Sense can account for the impact of this conduction on the next room and empirically build a physics-based model of the thermal relationship between these rooms. Similarly, it can calculate the capacity and lag of thermal masses and, consequently, account for their impact on control systems. 

Q Sense captures detailed data representing a snapshot in time that is used to build a model and then analyzed across a range of operating conditions such as seasons, weather and occupancy levels. This works because Q Sense measures everything in a space and then calculates its impact on building systems, controls and occupants. For example, higher occupancy can be simulated by placing human-shaped ~100 watt heaters in the space and redoing the heat transfer calculations (this is simplified for clarity).

The data sets are physically located and oriented using GPS data from the iPhone. This allows Q Sense to pull in external data such as local weather (external air temperature, humidity, day/night cycle etc.) and solar irradiance. This exogenous data is combined with the internal thermal point cloud to form a complete thermal model. Additionally, using local weather patterns, Q Sense can simulate the day/night cycle and seasonal variation for a building from a single scan.

WEBSITE: HearthLabs.com