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1. Unobtrusive occupancy and vital signs sensing for human building interactive systems

   https://doi.org/10.1038/s41598-023-27425-6  

   Song, Chenyan ; Droitcour, Amy D. ; Islam, Shekh M. ; Whitworth, Avon ; Lubecke, Victor M. ; Boric-Lubecke, Olga ( December 2023 , Scientific Reports)

   Abstract Cognitive buildings use data on how occupants respond to the built environment to proactively make occupant-centric adjustments to lighting, temperature, ventilation, and other environmental parameters. However, sensors that unobtrusively and ubiquitously measure occupant responses are lacking. Here we show that Doppler-radar based sensors, which can sense small physiological motions, provide accurate occupancy detection and estimation of vital signs in challenging, realistic circumstances. Occupancy was differentiated from an empty room over 93% of the time in a 3.4 m × 8.5 m conference room with a single sensor in both wall and ceiling-mounted configurations. Occupancy was successfully detected while an occupant was under the table, visibly blocked from the sensor, a scenario where infrared, ultrasound, and video-based occupancy sensors would fail. Heart and respiratory rates were detected in all seats in the conference room with a single ceiling-mounted sensor. The occupancy sensor can be used to control HVAC and lighting with a short, 1–2 min delay and to provide information for space utilization optimization. Heart and respiratory rate sensing could provide additional feedback to future human-building interactive systems that use vital signs to determine how occupant comfort and wellness is changing with time. 

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2. Energy Co-simulation of the Hybrid Cooling Control with Synthetic Thermal Preference Distributions

   Lu, S. ; Zhiang, Z. ; Hameen, E. C. ; Lartigue, B. ; Karaguzel, O. ( January 2020 , Symposium on Simulation in Architecture and Urban Design (SIMAUD 2020))

   Thermal comfort and energy efficiency are always the two most significant objectives in HVAC operations. However, for conventional HVAC systems, the pursuit of high energy efficiency may be at the expense of satisfactory thermal comfort. Therefore, even if centralized HVAC systems nowadays have higher energy efficiency than before in office buildings, most of them cannot adapt the dynamic occupant behaviors or individual thermal comfort. In order to realize high energy efficiency while still maintain satisfactory thermal environment for occupants indoors, the integrated hybrid HVAC system has been developed for years such as task-ambient conditioning system. Moreover, the occupant-based HVAC control system such as human- in-the-loop has also been investigated so that the system can be adaptive based on occupant behaviors. However, most of research related to personalized air-conditioning system only focuses on field-study with limited scale (i.e. only one office room), this paper has proposed a co- simulation model in energyplus to simulate the hybrid cooling system with synthetic thermal comfort distributions based on global comfort database I&II. An optimization framework on cooling set-point is proposed with the objective of energy performance and the constraints of thermal comfort distribution developed by unsupervised Gaussian mixture model (GMM) clustering and kernel density estimation (KDE). The co-simulation results have illustrated that with the proposed optimization algorithm and the hybrid cooling system, HVAC demand power has decreased 5.3% on average with at least 90% of occupants feeling satisfied. 

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3. What drives our behaviors in buildings? A review on occupant interactions with building systems from the lens of behavioral theories

   https://doi.org/10.1016/j.buildenv.2020.106928  

   Arsalan Heydarian, Claire McIlvennie ( July 2020 , Building and environment)

   Occupant behavior has a significant impact on building systems’ operations and efficiency. As a result, several innovative approaches have been introduced to quantify the dynamics of occupants within indoor environments, such as interactions with different building systems and the impact of various feedback and interventions to reduce the building energy consumption. To achieve this, researchers have highlighted the importance of reducing energy consumption without impacting occupant comfort. As a result, there is an increasing body of research evaluating how different theories of behavior across a variety of disciplines can explain occupant interactions with building systems. Future progress in this area calls for an in-depth understanding of behavioral theories in explaining occupant interactions with different building systems. In this paper, we have used a structured literature review approach to investigate how different psychological, sociological, and economic theories have been applied to explain occupant interactions with heating and cooling (HVAC systems), opening windows and ventilation, lighting and shading, electronic appliances, domestic hot water, as well as energy conservation behaviors. Throughout the paper, we identify the most common theories and methodologies applied within the existing research, general findings related to how occupants interact with different building systems, as well as a number of identified gaps within the literature. Finally, we provide a discussion on directions for future research studies in this area under each building system. 

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4. HISDAC-ES: Historical Settlement Data Compilation for Spain (1900-2020)

   https://doi.org/10.6084/m9.figshare.22009643.v2  

   Uhl, Johannes H. ; Royé, Dominic ; Burghardt, Keith ; Aldrey Vázquez, José Antonio ; Borobio Sanchiz, Manuel Sanchiz ; Leyk, Stefan ( January 2023 , figshare)

   The historical settlement data compilation for Spain (HISDAC-ES) is a geospatial dataset consisting of over 240 gridded surfaces measuring the physical, functional, age-related, and evolutionary characteristics of the Spanish building stock. We scraped, harmonized, and aggregated cadastral building footprint data for Spain, covering over 12,000,000 building footprints including construction year attributes, to create a multi-faceted series of gridded surfaces (GeoTIFF format), describing the evolution of human settlements in Spain from 1900 to 2020, at 100m spatial and 5 years temporal resolution. Also, the dataset contains aggregated characteristics and completeness statistics at the municipality level, in CSV and GeoPackage format.

   !!! UPDATE 08-2023 !!!: We provide a new, improved version of HISDAC-ES. Specifically, we fixed two bugs in the production code that caused an incorrect rasterization of the multitemporal BUFA layers and of the PHYS layers (BUFA, BIA, DWEL, BUNITS sum and mean). Moreover, we added decadal raster datasets measuring residential building footprint and building indoor area (1900-2020), and provide a country-wide, harmonized building footprint centroid dataset in GeoPackage vector data format.

   File descriptions:

   Datasets are available in three spatial reference systems:

   1. HISDAC-ES_All_LAEA.zip: Raster data in Lambert Azimuthal Equal Area (LAEA) covering all Spanish territory.
   2. HISDAC-ES_IbericPeninsula_UTM30.zip: Raster data in UTM Zone 30N covering all the Iberic Peninsula + Céuta and Melilla.
   3. HISDAC-ES_CanaryIslands_REGCAN.zip: Raster data in REGCAN-95, covering the Canary Islands only.
   4. HISDAC-ES_MunicipAggregates.zip: Municipality-level aggregates and completeness statistics (CSV, GeoPackage), in LAEA projection.
   5. ES_building_centroids_merged_spatjoin.gpkg: 7,000,000+ building footprint centroids in GeoPackage format, harmonized from the different cadastral systems, representing the input data for HISDAC-ES. These data can be used for sanity checks or for the creation of further, user-defined gridded surfaces.

   Source data:

   HISDAC-ES is derived from cadastral building footprint data, available from different authorities in Spain:

   • Araba province: https://geo.araba.eus/WFS_Katastroa?SERVICE=WFS&VERSION=1.1.0&REQUEST=GetCapabilities
   • Bizkaia province: https://web.bizkaia.eus/es/inspirebizkaia
   • Gipuzkoa province: https://b5m.gipuzkoa.eus/web5000/es/utilidades/inspire/edificios/
   • Navarra region: https://inspire.navarra.es/services/BU/wfs
   • Other regions: http://www.catastro.minhap.es/INSPIRE/buildings/ES.SDGC.bu.atom.xml
   • Data source of municipality polygons: Centro Nacional de Información Geográfica (https://centrodedescargas.cnig.es/CentroDescargas/index.jsp)

   Technical notes:

   Gridded data

   File nomenclature:

   ./region_projection_theme/hisdac_es_theme_variable_version_resolution[m][_year].tif

   Regions:

   • all: complete territory of Spain
   • can: Canarian Islands only
   • ibe: Iberic peninsula + Céuta + Melilla

   Projections:

   • laea: Lambert azimuthal equal area (EPSG:3035)
   • regcan: REGCAN95 / UTM zone 28N (EPSG:4083)
   • utm: ETRS89 / UTM zone 30N (EPSG:25830)

   Themes:

   • evolution / evol: multi-temporal physical measurements
   • landuse: multi-temporal building counts per land use (i.e., building function) class
   • physical / phys: physical building characteristics in 2020
   • temporal / temp: temporal characteristics (construction year statistics)

   Variables: evolution

   • budens: building density (count per grid cell area)
   • bufa: building footprint area
   • deva: developed area (any grid cell containing at least one building)
   • resbufa: residential building footprint area
   • resbia: residential building indoor area

   Variables: physical

   • bia: building indoor area
   • bufa: building footprint area
   • bunits: number of building units
   • dwel: number of dwellings

   Variables: temporal

   • mincoy: minimum construction year per grid cell
   • maxcoy: minimum construction year per grid cell
   • meancoy: mean construction year per grid cell
   • medcoy: median construction year per grid cell
   • modecoy: mode (most frequent) construction year per grid cell
   • varcoy: variety of construction years per grid cell

   Variable: landuse

   Counts of buildings per grid cell and land use type.

   Municipality-level data

   • hisdac_es_municipality_stats_multitemporal_longform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in long form. Note that a value of 0 for the year attribute denotes the statistics for records without construction year information.
   • hisdac_es_municipality_stats_multitemporal_wideform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in wide form. Note that a value of 0 for the year suffix denotes the statistics for records without construction year information.
   • hisdac_es_municipality_stats_completeness_v1.csv: This CSV file contains the missingness rates (in %) of the building attribute per municipality, ranging from 0.0 (attribute exists for all buildings) to 100.0 (attribute exists for none of the buildings) in a given municipality.

   Column names for the completeness statistics tables:

   • NATCODE: National municipality identifier*
   • num_total: number of buildings per munic
   • perc_bymiss: Percentage of buildings with missing built year (construction year)
   • perc_lumiss: Percentage of buildings with missing landuse attribute
   • perc_luother: Percentage of buildings with landuse type "other"
   • perc_num_floors_miss: Percentage of buildings without valid number of floors attribute
   • perc_num_dwel_miss: Percentage of buildings without valid number of dwellings attribute
   • perc_num_bunits_miss: Percentage of buildings without valid number of building units attribute
   • perc_offi_area_miss: Percentage of buildings without valid official area (building indoor area, BIA) attribute
   • perc_num_dwel_and_num_bunits_miss: Percentage of buildings missing both number of dwellings and number of building units attribute

   The same statistics are available as geopackage file including municipality polygons in Lambert azimuthal equal area (EPSG:3035).

   *From the NATCODE, other regional identifiers can be derived as follows:

   • NATCODE: 34 01 04 04001
   • Country: 34
   • Comunidad autónoma (CA_CODE): 01
   • Province (PROV_CODE): 04
   • LAU code: 04001 (province + municipality code)

    

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5. ENENGY MANAGEMENT OF SMART COMMUNITY WITH EV CHARGING USING DISTRIBUTED MODEL PREDICTIVE CONTROL

   Fenglin Zhou, Yaoyu Li ( October 2018 , Proceedings of ASME 2018 Dynamic Systems and Control Conference)

   Human activities in buildings are connected by various transportation measures. For the emerging Smart and Connected Communities (S&CC), it is possible to synergize the energy management of smart buildings with the vehicle operation/travel information available from transportation infrastructure, e.g. the intelligent transportation systems (ITS). Such information enables the prediction of upcoming building occupancy and upcoming charging load of electrified vehicles. This paper presents a predictive energy management strategy for smart community with a distributed model predictive control framework, in which the upcoming building occupancy and charging load are assumed to be predictable to certain extent based on the ITS information. An illustrative example of smart community is used for simulation study based on a Modelica simulation model, in which a chilled-water plant sustains the ventilation and air conditioning of three buildings, and each building is assumed to host a number of charging stations. Simulation study is performed to validate the proposed strategy. 

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