Ireland - Net occupancy rate in hotels and similar accommodations: Bedrooms was 77.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Ireland - Net occupancy rate in hotels and similar accommodations: Bedrooms - last updated from the EUROSTAT on August of 2025. Historically, Ireland - Net occupancy rate in hotels and similar accommodations: Bedrooms reached a record high of 77.00% in December of 2024 and a record low of 17.00% in December of 2021.

An occupancy model makes use of data that are structured as sets of repeated visits to each of many sites, in order estimate the actual probability of occupancy (i.e., proportion of occupied sites) after correcting for imperfect detection using the information contained in the sets of repeated observations. We explore the conditions under which preexisting, volunteer-collected data from the citizen science project eBird can be used for fitting occupancy models. The data archived here are used to explore two ways in which the single-visit records could be used in occupancy models. First, we use empirical data contained within this archive to assess the potential for space-for-time substitution: aggregating single-visit records from different locations within a region into pseudo-repeat visits. The archived data are used to illustrate that the locations chosen for data collection by observers were not always representative of the habitat in the surrounding area, which would lead to biased...

The occupancy rates of bedrooms in hotels in Ireland reach a new high with 72 percent utilization. This was an increase of two percent when compared with the previous year, and an increased of 14 percent since 2012.

Ireland IE: Net Occupancy Rate: Bedrooms data was reported at 81.000 % in Oct 2024. This records a decrease from the previous number of 89.000 % for Sep 2024. Ireland IE: Net Occupancy Rate: Bedrooms data is updated monthly, averaging 61.000 % from Jan 2012 (Median) to Oct 2024, with 118 observations. The data reached an all-time high of 91.000 % in Aug 2024 and a record low of 0.000 % in May 2020. Ireland IE: Net Occupancy Rate: Bedrooms data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s Ireland – Table IE.Eurostat: Bed Places and Bedrooms Net Occupancy Rate. Net occupancy rate covers hotels and similar accommodation.

Ireland - Net occupancy rate in hotels and similar accommodations: Bedplaces was 58.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Ireland - Net occupancy rate in hotels and similar accommodations: Bedplaces - last updated from the EUROSTAT on July of 2025. Historically, Ireland - Net occupancy rate in hotels and similar accommodations: Bedplaces reached a record high of 59.00% in December of 2023 and a record low of 13.00% in December of 2021.

Ireland IE: Net Occupancy Rate: Bed Places data was reported at 40.000 % in Dec 2018. This records a decrease from the previous number of 46.000 % for Nov 2018. Ireland IE: Net Occupancy Rate: Bed Places data is updated monthly, averaging 45.000 % from Jan 1991 (Median) to Dec 2018, with 216 observations. The data reached an all-time high of 74.000 % in Aug 2017 and a record low of 24.000 % in Jan 1991. Ireland IE: Net Occupancy Rate: Bed Places data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s Ireland – Table IE.Eurostat: Bed Places and Bedrooms Net Occupancy Rate.

In 2022, Dublin had the highest number of housing units in Ireland, followed by Cork and Fingal. Nearly ******* of the total ******* homes in Dublin were occupied. Holiday homes amounted to ****** units, while other vacant dwellings and temporarily vacant homes were ****** and *****, respectively.

This statistic shows the bedroom occupancy rate in serviced accommodation establishments in Northern Ireland from 2011 to 2021. The annual occupancy rate in Northern Ireland was ** percent in 2021.

This statistic presents the room and bed-space occupancy rate in guest accommodation in Northern Ireland from 2015 to 2021. The occupancy rate of rooms and bed-spaces saw an overall increase between 2020 and 2021. Rooms increased to ** percent and bed-spaces increased to ** percent.

The availability of the building’s operation data and occupancy information has been crucial to support the evaluation of existing models and development of new data driven approaches. This paper describes a comprehensive dataset consisting of indoor environmental conditions, Wi-Fi connected devices, energy consumption of end uses (i.e., HVAC, lighting, plug loads and fans), HVAC operations, and outdoor weather conditions collected through various heterogeneous sensors together with the ground truth occupant presence and count information for five rooms located in a university environment. The five rooms include two different-sized lecture rooms, an office space for administrative staff, an office space for researchers, and a library space accessible to all students. A total of 181 days of data was collected from all five rooms at a sampling resolution of 5 minutes. This dataset can be used for benchmarking and supporting data-driven approaches in the field of occupancy prediction and occupant behaviour modelling, building simulation and control, energy forecasting and various building analytics.

If you are interested in using this dataset, please cite our work as follows: Tekler, Zeynep Duygu, et al. "ROBOD, room-level occupancy and building operation dataset." Building Simulation. Tsinghua University Press, 2022. https://doi.org/10.1007/s12273-022-0925-9

This file contains variables from the Housing Theme - Occupancy that was produced by AIRO using data from the census unit at the CSO and the Northern Ireland Research and Statistics Agency (NISRA). This data was developed under the Evidence Based Planning theme of the Ireland Northern Cross Border Cooperation Observatory (INICCO-2) and CrosSPlaN-2 funded research programme.

The Commercial Occupancy Dataset (COD) is a high-resolution long-term dataset of occupancy traces in a commercial office building spanning 9 months and covering room-level occupancy for three different spaces (two conference rooms and one open-plan space) containing more than 90,000 enter/exit events over this time period. Occupancy data in a building contains rich spatial-temporal information about the users and their usage of the space and facilities. However, obtaining accurate occupancy data is a very challenging task due to the limitation of existing sensing technologies. A novel depth-imaging based solution to estimate occupancy counts was deployed in four doorways of an office building to generate the dataset. We envision the dataset being used for diverse applications such as building energy simulation, occupancy modeling and human-in-the-loop HVAC control which enhance energy efficiency and human comfort.

Each folder in the dataset represents data from a single building. Inside the folder there will be separate comma-separated value (CSV) files, one for each monitored room within the building. Each CSV file contains an entry for every entrance and exit events that was estimated by the sensor(s) corresponding to the room in question. In turn, each one of these entries in the dataset (i.e., each line in the file) contains three fields in this order: date (m/dd/yy), time (HH:MM:SS) and occupancy count.

Please be advised that there are issues with the Small Area boundary dataset generalised to 20m which affect Small Area 268014010 in Ballygall D, Dublin City. The Small Area boundary dataset generalised to 20m is in the process of being revised and the updated datasets will be available as soon as the boundaries are amended.This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Small Areas national boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 6.8, occupancy status of permanent dwellings on Census night. Attributes include dwellings by occupancy status (e.g. occupied, temporarily absent, unoccupied holiday homes). Census 2016 theme 6 represents Housing. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO. The Small Area Boundaries were created with the following credentials. National boundary dataset. Consistent sub-divisions of an ED. Created not to cross some natural features. Defined area with a minimum number of GeoDirectory building address points. Defined area initially created with minimum of 65 – approx. average of around 90 residential address points. Generated using two bespoke algorithms which incorporated the ED and Townland boundaries, ortho-photography, large scale vector data and GeoDirectory data. Before the 2011 census they were split in relation to motorways and dual carriageways. After the census some boundaries were merged and other divided to maintain privacy of the residential area occupants. They are available as generalised and non generalised boundary sets.

This is the dataset used for the publication "Coddora: CO2-based Occupancy Detection modeltrained via DOmain RAndomization". The goal is to provide training data for occupancy detection.The dataset contains one million days of data including 10 occupied days for each of 100,000 randomized room models (50,000 rooms considering office activity and 50,000 meeting room activity). Data were generated in EnergyPlus simulations according to the methodology described in the paper.When using the dataset, please cite:

Manuel Weber, Farzan Banihashemi, Davor Stjelja, Peter Mandl, Ruben Mayer, and Hans-Arno Jacobsen. 2024. Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization. In International Joint Conference on Neural Networks (IJCNN). June 30 - July 5, 2024, Yokohama, Japan.

Dataset Structure

The following files are provided: 1. dataset_office_rooms.h5 (provided as zip file) 2. dataset_meeting_rooms.h5 (provided as zip file) 3. simulated_occupancy_office_rooms.csv 4. simulated_occupancy_meeting_rooms.csv

Please use an archiving tool such as 7zip to unzip the hdf5 files.Both hdf5 files contain two datasets with the following keys: 1. "data": contains the simulated indoor climate and occupancy data 2. "metadata": contains the metadata that were used for each simulation

The csv files contain the time series of occupancy that were used for the simulations.

Data

Data includes the following fields:

Datetime: day of the year (may be relevant due to seasonal differences) and time of the dayZone Air CO2 Concentration: CO2 level in ppmZone Mean Air Temperature: temperature in °CZone Air Relative Humidity: relative humidity in %Occupancy: level of occupancy relative to the maximum capacity of the room (in the range [0-1])Ventilation: fraction of window opening in the range [0.01, 1]SimID: foreign key to reference the room properties the simulation was based onBinaryOccupancy: 0 or 1 denoting absence or presence (for binary classification)

Example row:

Datetime Zone Air CO2 Concentration Zone Mean Air Temperature Zone Air Relative Humidity Occupancy Ventilation simID BinaryOccupancy

10/09 11:21:00

1084.5624647371608

24.545635909907148

41.18393114737054

0.7

0.0

99 1

Metadata

Metadata includes the following fields. Underscores denote that the field was not selected during randomization but calculated from the other values.

width: room width in mlength: room length in mheight: hoom height in minfiltration: infiltration per exterior area in m³/m²soutdoor_co2: co2 concentration in the outdoor air in ppm (set to a random value between [300, 500])orientation: angle between the room's facade orientation and the north direction in degreesmaxOccupants: room occupation limit, i.e. the maximum number of occupants_floorArea: floor area in m² (calculated from room dimensions)_volume: room volume in m³ (calculated from room dimensions)_exteriorSurfaceArea: surface area of the facade wall (calculated from room dimensions)_winToFloorRatio: ratio between total window area and floor area (calculated from room model)firstDayUsedOfOccupancySequence: selected starting day in the sequence of occupancy data for rooms with the respective maxOccupants valuesimID: unique identifier of the simulation to relate between simulation metadata and resulting simulated data

Example row:

width length height infiltration outdoor_co2 orientation maxOccupants _floorArea _volume _exteriorSurfaceArea _winToFloorRatio firstDayOfUsedOccupancySequence simID

5.481 5.190 3.264 0.000214 438.0 316.0 4.0 28.446 92.849 16.940 0.216 192 0

Occupancy Data

The occupancy data provided through the separate csv files contain the data from the upfront occupancy simulations that the climate simulation was based on. For each level of considered room occupancy limit (maxOccupants), the datasets provide minute values of occupancy throughout 1000 days.

Datetime, Date, Timestamp: fictive time of simulated occupancy record (sequences are in 1-minute resolution)Occupants: number of present occupantsOccupancy: binary occupancy state (0=unoccupied, 1=occupied)WindowState: binary state of ventilation (0=windows closed, 1=room is ventilated)maxOccupants: maximum number of occupants considered for the simulated sequenceWindowOpeningFraction: fractional extent to which windows are opened, within the interval [0.01, 1]

Example row:

Datetime Date Timestamp Occupants Occupancy WindowState maxOccupants WindowOpeningFraction

2023-01-01 00:00:00 2023-01-01 1.672531e+09 0 0 0 1 0.0

This page contains an API with real-time data on the occupancy of Accessible Parking Bays in the DLR jurisdiction. The data is for a selection of Parking Bays with sensors installed. The occupancy status is pulled every minute.

This feature layer was created using Census 2016 data produced by the Central Statistics Office (CSO) and Local Electoral Area boundary data (generalised to 20m) produced by Tailte Éireann. The layer represents Census 2016 theme 6.8, occupancy status of permanent dwellings on Census night. Attributes include dwellings by occupancy status (e.g. occupied, temporarily absent, unoccupied holiday homes). Census 2016 theme 6 represents Housing. The Census is carried out every five years by the CSO to determine an account of every person in Ireland. The results provide information on a range of themes, such as, population, housing and education. The data were sourced from the CSO.For the purposes of County Council and Corporation elections each county and city is divided into Local Electoral Areas (LEAs) which are constituted on the basis of Orders made under the Local Government Act, 1941. In general, LEAs are formed by aggregating Electoral Divisions. However, in a number of cases Electoral Divisions are divided between LEAs to facilitate electors. The current composition of the LEAs have been established by Statutory Instruments No’s 427-452/2008, 503-509/2008 and 311/1998.

In 2019, the room occupancy rate of hotels in Ireland was at ** percent. Meanwhile, the bed occupancy rate of hostels was at ** percent.

This statistic displays the room and bed-space occupancy of serviced accommodation in Northern Ireland monthly from 2020 to 2021. In December 2021, the room occupancy rate was ** percent and the bed-space occupancy rate was ** percent.