Engineering topics

Huizenga, Charlie

Publications and source records attributed to Huizenga, Charlie.

3 records

DOE OSTI2024

Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning

The ability to measure occupants’ thermal state in real time will enable major advances in the control of air conditioning systems. This study proposes predicting occupant thermal state by a combination of infrared thermography, computer vision, and machine learning. The approach (1) uses cheek, nose, and hand temperatures because they are least subject to blockage by hair, glasses, and clothing; (2) measures the distribution of skin temperatures within geometrically defined sub-areas of the face and hand; and (3) uses temperature differences within and between these areas to eliminate the effects of calibration drift that are unavoidable in thermal infrared (TIR) cameras. Two series of tests were conducted, respectively in an outdoor carport and an indoor environmental chamber, collecting a total of 48,422 sets of cheek, nose, and hand skin temperatures using a TIR camera and computer-vision technology, coupled with 715 subjective responses of thermal sensations. To predict occupant thermal state, Random Forest classification models were built using either absolute skin temperatures (the maximum and median temperatures of cheek and hand segments, and the temperature of the central spot on the nose), or intra- and inter-segment temperature differences of cheeks, hands, and nose. These measurements were found to accurately predict occupant thermal state. Using the maximum and median temperatures for cheek and nose, or for cheek and hand, predicts thermal state with an accuracy of 92–96%. In conclusion, using only the intra- and inter-segment temperature differences from cheek and nose is 83% accurate; adding the hand temperature differences increases the accuracy to 96%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Berkeley Lab WINDOW

WINDOW features include: - Microsoft Windows TM interface - algorithms for the calculation of total fenestration product U-values and Solar Heat Gain Coefficient consistent with ASHRAE SPC 142, ISO 15099, and the National Fenestration Rating Council - a Condensation Resistance Index in accordance with the NFRC 500 Standard - and integrated database of properties - imports data from other LBNL window analysis software: - Import THERM file into the Frame Library - Import records from IGDB and OPtics5 into the Glass Library for the optical properties of coated and uncoated glazings, laminates, and applied films. Program Capabilities WINDOW 7.2 offers the following features: The ability to analyze products made from any combination of glazing layers, gas layers, frames, spacers, and dividers under any environmental conditions and at any tilt; The ability to model complex glazing systems such as venetian blinds and roller shades. Directly accessible libraries of window system components, (glazing systems, glazing layers, gas fills, frame and divider elements), and environmental conditions; The choice of working in English (IP), or Systeme International (SI) units; The ability to specify the dimensions and thermal properties of each frame element (header, sills, jamb, mullion) in a window; A multi-band (wavelength-by-wavelength) spectral model; A Glass Library which can access spectral data files for many common glazing materials from the Optics5database; A night-sky radiative model; A link with the DOE-2.1E and Energy Plus building energy analysis program. Performance Indices and Other Results For a user-defined fenestration system and user-defined environmental conditions, WINDOW calculates: The U-value, solar heat gain coefficient, shading coefficient, and visible transmittance for the complete window system; The U-value, solar heat gain coefficient, shading coefficient, and visible transmittance for the glazing system (center-of-glass values); The U-values of the frame and divider elements and corresponding edge-of-glass areas (based on generic correlations); The total solar and visible transmittance and reflectances of the glazing system. Color properties, i.e. L*, a*, and b* color coordinates, dominant wavelength, and purity for transmitted and reflected (outdoor) solar radiation; The damage-weighted transmittance of the glazing system between 0.3 an 0.38 microns; The angular dependence of the solar and visible transmittances, solar and visible reflectances, solar absorptance, and solar heat gain coefficient of the glazing system; The percent relative humidity of the inside and outside air for which condensation will occur on the interior and exterior glazing surfaces respectively; The center-of-glass temperature distribution.

Curcija, DraganCharlie↗