State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Linking human-building interactions in shared offices with personality traits

Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. The study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment

Visualizing urban microclimate and quantifying its impact on building energy use in San Francisco

Weather data at nearby airports are usually used in building energy simulation to estimate energy use in buildings or evaluate building design or retrofit options. However, due to urbanization and geography characteristics, local weather conditions can differ significantly from those at airports. This study presents the visualization of 10-year hourly weather data measured at 27 sites in San Francisco, aiming to provide insights into the urban microclimate and urban heat island effect in San Francisco and how they evolve during the recent decade. The 10-year weather data are used in building energy simulations to investigate its influence on energy use and electrical peak demand, which informs the city's policy making on building energy efficiency and resilience. The visualization feature is implemented in CityBES, an open web-based data and computing platform for urban building energy research

Heavy-tailed statistics in short-message communication

Short-message (SM) is one of the most frequently used communication channels in the modern society. In this Brief Report, based on the SM communication records provided by some volunteers, we investigate the statistics of SM communication pattern, including the interevent time distributions between two consecutive short messages and two conversations, and the distribution of message number contained by a complete conversation. In the individual level, the current empirical data raises a strong evidence that the human activity pattern, exhibiting a heavy-tailed interevent time distribution, is driven by a non-Poisson nature.Comment: 4 pages, 4 figures and 1 tabl

Specific heat and thermal conductivity of ferromagnetic magnons in Yttrium Iron Garnet

The specific heat and thermal conductivity of the insulating ferrimagnet Y$_3$Fe$_5$O$_{12}$ (Yttrium Iron Garnet, YIG) single crystal were measured down to 50 mK. The ferromagnetic magnon specific heat $C$$_m$ shows a characteristic $T^{1.5}$ dependence down to 0.77 K. Below 0.77 K, a downward deviation is observed, which is attributed to the magnetic dipole-dipole interaction with typical magnitude of 10$^{-4}$ eV. The ferromagnetic magnon thermal conductivity $\kappa_m$ does not show the characteristic $T^2$ dependence below 0.8 K. To fit the $\kappa_m$ data, both magnetic defect scattering effect and dipole-dipole interaction are taken into account. These results complete our understanding of the thermodynamic and thermal transport properties of the low-lying ferromagnetic magnons.Comment: 5 pages, 5 figure

Improving the security of secure direct communication based on secret transmitting order of particles

We analyzed the security of the secure direct communication protocol based on secret transmitting order of particles recently proposed by Zhu, Xia, Fan, and Zhang [Phys. Rev. A 73, 022338 (2006)], and found that this scheme is insecure if an eavesdropper, say Eve, wants to steal the secret message with Trojan horse attack strategies. The vital loophole in this scheme is that the two authorized users check the security of their quantum channel only once. Eve can insert another spy photon, an invisible photon or a delay one in each photon which the sender Alice sends to the receiver Bob, and capture the spy photon when it returns from Bob to Alice. After the authorized users check the security, Eve can obtain the secret message according to the information about the transmitting order published by Bob. Finally, we present a possible improvement of this protocol.Comment: 4 pages, no figur

Evaluation of human body comfortableness under vibrate condition by muscle oxygenation and surface electromyography (SEMG) parameter

To efficiently evaluate the influences on human body comfortableness under different vibrative condition, the paper comprehensively applied the surface electromyography (SEMG) and near infrared spectroscopy (NIRS) technology and obtained the relationship between mean power frequency (MPF) and regional tissue oxygen saturation (rSO2) and acceleration, frequency, time and amplitude through experiments on the variations of human body SEMG and muscle oxygenation under vibrative condition; analyzed and compared rules of variations of partial electromyography of the biceps brachii muscle, erector spinae muscle, biceps femories and tissue blood oxygen saturation depending on the changes of vibration parameter. With the analysis of the subjective evaluation, the author proposed the evaluation methods of human body comfortableness under different vibrate condition by muscle oxygenation and SEMG parameter. The study, with a certain degree of universalized quality, not onlyprovides a new referential ergonomic criterion for the man-machine interface design of important facilities like tank and aircrafts, but can also be applied in the design evaluation of transportation vehicles and the evaluation of comfortableness of work environment

Fully gapped superconducting state in Au2Pb: a natural candidate for topological superconductor

We measured the ultra-low-temperature specific heat and thermal conductivity of Au$_2$Pb single crystal, a possible three-dimensional Dirac semimetal with a superconducting transition temperature $T_c \approx$ 1.05 K. The electronic specific heat can be fitted by a two-band s-wave model, which gives the gap amplitudes $\Delta_1$(0)/$k_BT_c$ = 1.38 and $\Delta_2$(0)/$k_BT_c$ = 5.25. From the thermal conductivity measurements, a negligible residual linear term $\kappa_0/T$ in zero field and a slow field dependence of $\kappa_0/T$ at low field are obtained. These results suggest that Au$_2$Pb has a fully gapped superconducting state in the bulk, which is a necessary condition for topological superconductor if Au$_2$Pb is indeed one.Comment: 6 pages, 4 figure