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19 results for “smart buildings”
SMART Infrastructure Facility Building Data
<p><strong>SMART Building Data</strong></p> <p>Authors: J. Barthelemy, B. Arshard, N. Verstaevel, P. Perez<br> Contact: SMART Infrastructure Facility - smart-iot@uow.edu.au<br> Version: 08 July 2020</p> <p><strong>Description</strong></p> <p>The time series data has been generated by Droplet sensors installed in every room of the SMART Infrastructure Facility of the University of Wollongong. The sampling rate is set to one minute and the data is transmitted via a LoRaWAN network.</p> <p>Each device sense temperature, humidity, luminosity, pressure, movement and CO2 (*). In addition, they transmit their orientation (*), node id, room id and battery voltage. Each packet received by the LoRaWAN network is also characterized by a RSSI, an SNR and a checksum. The (*) CO2 and orientation data are not accurate.</p> <p><strong>Data dictionary</strong></p> <p>The dataset contains the following features:</p> <p>* info : the type of information for the current row. It can be: <br> - temp -> temperature (Celcius)<br> - humidity -> humidity (%)<br> - light -> luminosity (1024 levels, 0 being the darkest and 1024 the brightest)<br> - co2 -> CO2 (1024 levels, 0 being the lowest, 1024 the highest)<br> - pressure -> pressure (hPa)<br> - orient -> orientation of the sensor<br> - nodeId -> internal id of the sensor<br> - roomNum -> room id of the sensor<br> - voltage -> battery voltage of the sensor (V)<br> - movement -> motion detection (True/False)<br> - checksum -> checksum of the packet transmitted via LoRaWAN<br> * room_id : the room id in which the sensor is installed (see map of the building)<br> * date_time : the timestamp of the data<br> * bool_v : boolean value (true/false) for movement<br> * str_v : string value for nodeId, checksum, roomNum<br> * long_v : integer (long) value for light, orient, rssi, co2, humidity<br> * dbl_v : real (double) value for voltage, pressure, temp, snr</p> <p><strong>Notes</strong></p> <p>- The uncompressed dataset is a 40Gb CSV file.<br> - Droplet sensors specifications: https://nube-io.com/wp-content/uploads/Droplet-Specifications-V5.0-1.pdf.<br> - More information about the SMART Infrastructure Facility is available here: https://www.uow.edu.au/smart/.</p>
Optimised household consumption profiles through a smart building energy mangement system TABEDE
<p>In the context of the TABEDE project (<a href="https://www.tabede.eu/">https://www.tabede.eu/</a>) several synthetic profiles simulating the consumption and generation of residential buildings, whose appliances were controlled by our proposed Energy Management System (i.e., the TABEDE solution), were simulated. Their construction process was characterised by the following:</p> <ul> <li>Consumption profiles were generated via a bottom-up approach capable of emulating the consumption of individual household appliances. These last ones correspond to the most used appliances in the UK, which were randomly distributed among the buildings based on their average utilisation rate and ownership observed in residential buildings in the country.</li> <li>The physics in terms of heat exchange between neighbouring buildings and the environment were considered, together with the size of the buildings and their physical characteristics. A total of 66 houses and apartments, according to 8 type or building archetypes were created.</li> <li>PV generation profiles were generated according to the meteorological condition of the simulated day.</li> </ul> <p>Together with this, the profiles feature how the TABEDE solution optimised the flexible part of the consumption (i.e., appliances that were controllable by the solution and whose consumption could be shifted in time without sacrificing user comfort) to minimize the electricity bill of the buildings.</p> <p>The information contained in the actual database features the following variables:</p> <ul> <li>TABEDE penetration: percentage of buildings owning the TABEDE solution. Buildings with TABEDE will observe their flexible consumption being optimised.</li> <li>PV penetration: percentage of buildings with a PV system installed on them.</li> <li>Simulation day: one day in summer (19/06/2019) featuring the highest solar radiation of the year, and a day in winter (19/12/2019) with the lowest.</li> <li>Batteries: whether the PV systems is installed alongside household batteries.</li> </ul> <p>Details on the formulation can be found in: <a href="https://urldefense.com/v3/__https:/www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/__;!!La4veWw!khYBEaeJY85mX5yQUrp0PwoXcg5U10dEdgZ296hONYGyBS5xg91Z8MoDUQy34a4f9Lo$">https://www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/</a></p>
A full-year data regarding a smart building
<p>The smart building represented by the dataset is divided into five zones, where each zone has 2 to 3 offices. The building has photovoltaic panels with a generation peak of 7.5 kW, inside sensors, light intensity control, and all consumption measured by load type. The data was collected in 5 minutes periods.</p> <p>By zones, the buildings have a distribution of 6 researchers in Zone#1, 5 researchers in Zone#2, 5 researchers in Zone#3, 3 researchers in Zone#4, and 5 researchers in Zone#5. Zone#1 includes a meeting room, and Zone#2 includes a server room. Regarding the server room, its HVAC unit was measured in HVAC#2, however, near the end of the year, the unit was removed from the monitoring system.</p> <p>The light intensity values represent the current state of the lamps and have a linear correlation with the lamp’s consumption.</p> <p>The dataset represents raw data without any treatment, this means that it is possible to find errors. The data do not have missing reading periods, but they can have a fixed zero (0) value, indicating a failure in the system. You can assume that periods with zero voltage represent an error in that zone at that period.</p> <p> </p> <p><br>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Dataset of IoT-Based Energy and Environmental Parameters in a Smart Building Infrastructure
<p>This dataset includes detailed measurements from IoT sensors deployed throughout the M5 building, capturing energy consumption from various devices like coffee machines, microwaves, etc., as well as environmental data such as temperature, humidity, and occupancy in key areas like the Interdisciplinary lab, kitchen, and mailroom.</p> <p>This release aims to provide researchers and practitioners with comprehensive data to facilitate research on energy efficiency and environmental monitoring within smart building infrastructures. The data are structured to support various types of analysis, from operational efficiency assessments to environmental impact studies.</p> <p>For detailed information on the dataset's structure and usage, please refer to the README.md file included in this repository.</p>
Smart Building Model (SBuM) Data
<p>This dataset is to be used in conjonction with the SBuM model available online at:</p> <p>https://github.com/jeanlouisnico/SBuM</p> <p>Download add the file into the path of your MatLab environment to allow MatLab to locate the files.</p> <p>v1.1: added the zip file that it is easier to download all the files at once.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 1. The whole-building switch concept for the power lines of standby devices
<p>68 million houses in North America and Europe will be smart by 2019 (Kurkinen, 2016) with a compound annual growth rate of 37 % and 61 %, respectively. The smart equipment is usually installed together with an upgrade (e.g. aluminum wires are replaced by copper ones) of the power grid. In this case, additional power lines for standby devices are cabled, and the WBS concept is applied using one power switch only (see figure 1). For instance, the Songle high-power relay T90 can control the whole building electricity with load up to 30 A using NodeMcu Lua ESP8266 WiFi and/or Arduino Uno / Mega boards.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 3. The unified hardware unit based on NodeMcu Lua ESP8266 WiFi development board, ACS712T ELC-30A current sensor, and relay SRD-05VDC-SL-C
<p>The software consists of two parts, low-level Arduino sketches and high-level C# Windows form appplication. They are connected using the open-source message MQTT broker Mosquitto.11 Every hardware unit has the unique identifier and commands to control the relay. The MQTT topic “/VPP/Relays” is used by subscribers and publishers. The number “50” sent from C# Windows form (it equals number “2” sent from the standard Mosquitto publisher) is a command to switch on the second relay, “51” (“3”) – to switch off, respectively. The prototype was developed with one root controller and two descendant relays. The commands are as follows: “52” (“4”) / “53” (“5”) – to switch on / off the first relay, “54” (“6”) / “55” (“7”) – to switch on / off the third relay, respectively. This solution is similar to the one presented in [22], but ACS712T ELC-30A current sensor and ESP8266WiFi.h library are applied here. In addition, other commands, e.g. “56” (“8”) to get the value of the current in the 3rd segment, are in use as well.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 5. An example of smart lighting using NodeMcu Lua ESP8266 ESP-12 WiFi board
<p> for different purposes together with switching on/off relays, e.g. to control the motors, to acquire the data from sensors. It allows developing multifunctional smart systems. For instance, the smart lighting unit is created using NodeMcu Lua ESP8266 ESP-12 WiFi board, Arduino light sensor, and relay SRD-05VDC-SL-C, which controls the power supply of the lamp. Figure 5 shows a simplified example of smart lighting, where the lamp is represented by eight 5 mm light-emitting diodes (LEDs).</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 4. Screen shot of the C# Windows form app
<p>The screen shot of the C# Windows form app is shown in figure 4. The text field on the left side includes numbers from 2 to 7, which are commands to control the states of relays. </p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 2. An example of smart power grid with hierarchical structure
<p> Figure 2 shows an example of smart power grid with hierarchical structure, where every segment equals a room or office. This approach is similar to the idea presented in Alboteanu et al. (2015), where the connecting / disconnecting of renewable energy sources and consumers are made via the appropriate contactors, automatically (or manually) controlled according to the energy consumption/generation. However, the management of micro smart grid is discussed in Alboteanu et al. (2015) only</p>
A full year of sensor data regarding a smart building room
<p>This dataset concerns a full year of clean data regarding a room in a smart building. The data considers the following: Outside temperature (x10)(ºC), Temperature 103 (x10)(ºC), Humidity 103 (x10)(%), Heat Index 103 (x10)(ºC), Occupation, AC status 103 (bool).</p> <p>The columns idenfied by x10 indicate that their value was multiplied by 10, to observe the raw value, please divide it by 10.</p>
An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting
<p>This repository is related with the next paper. Please cite us if this code is useful to you.</p> <p>Barral, V., Dominguez-Bolano, T., Escudero, C. J., & Garcia-Naya, J. A. An IoT System for Smart Building Combining Multiple mmWave FMCW Radars Applied to People Counting.</p> <h2>Python Scripts</h2> <ul> <li><em>count_plot.py</em>: Generates a plot comparing image tracking estimation, radar with DBSCAN, and radar with OPTICS. Use example:</li> </ul> <pre><code>python count_plot.py count_video_full.log count_radar_moving_average_30_optics.log count_radar_moving_average_30_dbscan.log "Video" "Radar with OPTICS" "Radar with DBSCAN" average_count_full_optics_dbscan.pdf </code></pre> <h2>Radar measurements</h2> <ul> <li><em>test_0.bag</em>: FMCW mmWave radar measurements from three IWR6843 devices (ISK and AOP). Is a ROS (Robotic Operative System) log, can be played with <code>rosbag play test_0.bag</code></li> </ul> <h3>Radar people counting logs</h3> <ul> <li> <p><strong>count_radar_moving_average_30_dbscan.log</strong>: radar people counting estimation using a 30 seconds moving average and DBSCAN as clustering algorithm.</p> </li> <li> <p><strong>count_radar_moving_average_30_optics.log</strong>: radar people counting estimation using a 30 seconds moving average and OPTICS as clustering algorithm.</p> </li> </ul> <h2>Image tracking</h2> <h3>Videos</h3> <ul> <li><em>test_0_cam_0_anonymized.mp4</em>: Camera 0 capture.</li> <li><em>test_0_cam_1_anonymized.mp4</em>: Camera 1 capture.</li> </ul> <h3>Image tracking logs</h3> <ul> <li><em>test_0_cam_0_interp.txt</em>: People count in camera 0</li> <li><em>test_0_cam_1_interp.txt</em>: People count in camera 1</li> </ul> <p>The files have the following format:</p> <p>frame_number, track_id, bb_left, bb_top, bb_width, bb_height, conf, x, y, z The coordinates x, y, z are always -1 The bb fields define the bounding box of the detection If multiple people are detected in a frame, there are several lines with that frame_number, but with different track_ids.</p> <h3>Image people counting logs</h3> <ul> <li><strong>count_video_full.log</strong>: image tracking people counting estimation using a 30 seconds moving average.</li> </ul>
A real-world energy management data set from a smart company building for optimization and machine learning
Open the record for dataset details and reuse information.
SMART Building Additional Data
<p><strong>SMART Building Additional Data</strong></p> <p>Authors: J. Barthelemy, B. Arshad, N. Verstaevel, D. Guastella, P. Perez<br> Contact: SMART Infrastructure Facility - smart-iot@uow.edu.au<br> Version: 09 July 2020</p> <p><strong>Description</strong></p> <p>The time series data has been generated by Droplet and Pycom sensors installed in the SMART Infrastructure Facility of the University of Wollongong.</p> <p>This dataset consist of</p> <ul> <li>a subset of the original complete SMART Infrastructure Facility Building Data (see 10.5281/zenodo.3935060)</li> <li>and additional data collected by the Pycom sensors</li> </ul> <p>See the floor plans for the disposition of the additional sensors.</p> <p><strong>Notes</strong></p> <p>- Droplet sensors specifications: https://nube-io.com/wp-content/uploads/Droplet-Specifications-V5.0-1.pdf<br> - More information about the SMART Infrastructure Facility is available here: https://www.uow.edu.au/smart/</p>
Development of strategy for combined smart ventilated window and PCM energy storage control for residential building energy saving
<p>The dataset includes the published data in the article Development of strategy for combined smart ventilated window and PCM energy storage control for residential building energy saving. For the details of the data description please refer to the paper.</p>
Three years of hourly data from 3021 smart heat meters installed in Danish residential buildings
<p>This dataset includes three years of cleaned hourly data from 3021 commercial smart heat meters installed in Danish residential buildings. The data are screened, interpolated to be equidistant, and missing values were imputed using a weighted moving average combined with a scaling algorithm to obey the data's cumulative trend. The original (anonymised) raw data of 3127 smart heat meters are also provided to increase transparency and reproducibility. Together with the consumption data, contextual information about the construction year, the type of building, and, if available, the energy label for the smart heat meters buildings (for all 3127 buildings of the raw data) are provided. The unique meter ID can link this data to the consumption data. A .pdf document describing the purpose of every data column is given in the folder '01_Data'. </p> <p>Besides this, three figures visualising the z-normalised data in different temporal resolutions are provided. Next to the data and the data visualisation, all code, written in R, used for data processing and extensive technical validation of the data is included.</p> <p>For a more extensive description, we would like to refer to our peer-reviewed open-access article describing the dataset and its processing: <a href="https://doi.org/10.1038/s41597-022-01502-3">https://doi.org/10.1038/s41597-022-01502-3</a>. Please also consider citing this article if you use this dataset. (Schaffer, M., Tvedebrink, T. & Marszal-Pomianowska, A. Three years of hourly data from 3021 smart heat meters installed in Danish residential buildings. Sci Data 9,<strong> </strong>420 (2022). https://doi.org/10.1038/s41597-022-01502-3)</p>
PLEIAData:consumption, HVAC (Heating, Ventilation & Air Conditioning), temperature, weather and motion sensor data for smart buildings applications
<p>This dataset presents detailed building operation data from the three blocks (A, B and C) of the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX. The aim of PHOENIX is to improve buildings efficiency, and therefore we included information of:<br> (i) consumption data, aggregated by block in kWh; (ii) HVAC (Heating, Ventilation and Air Conditioning) data with several features, such as state (ON=1, OFF=0), operation mode (None=0, Heating=1, Cooling=2), setpoint and device type; (iii) indoor temperature per room; (iv) weather data, including temperature, humidity, radiation, dew point, wind direction and precipitation; (v) carbon dioxide and presence data for few rooms; (vi) relationships between HVAC, temperature, carbon dioxide and presence sensors identifiers with their respective rooms and blocks. Weather data was acquired from the IMIDA (Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario).</p>
Tri-band electrochromic smart window for energy savings in buildings
Open the record for dataset details and reuse information.
GrowSmarter Smart building logistics with alternative fuelled vehicles Stockholm
<p>Baseline information from deliveries regarding building logistics center</p>
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