Cargando…
Dataset of usage pattern and energy analysis of an Internet of Things-enabled ceiling fan
Many electrical appliances have progressed from sheer prototypes to viable products by being automated with the help of sensors and Internet of Things (IoT). In this data driven century, there aren't many data-centric solutions for the effective use of residential and commercial ceiling fans. F...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876822/ https://www.ncbi.nlm.nih.gov/pubmed/36710914 http://dx.doi.org/10.1016/j.dib.2023.108900 |
_version_ | 1784878248533950464 |
---|---|
author | Khan, Hashim Raza Khalid, Muhammad Hashir bin Alam, Urooj Atiq, Mahnoor Qidwai, Uvais Qazi, Saad Ahmed |
author_facet | Khan, Hashim Raza Khalid, Muhammad Hashir bin Alam, Urooj Atiq, Mahnoor Qidwai, Uvais Qazi, Saad Ahmed |
author_sort | Khan, Hashim Raza |
collection | PubMed |
description | Many electrical appliances have progressed from sheer prototypes to viable products by being automated with the help of sensors and Internet of Things (IoT). In this data driven century, there aren't many data-centric solutions for the effective use of residential and commercial ceiling fans. For the said reason, sensors were installed on a remote-controlled BLDC ceiling fan, and a large amount of user data with environmental indicators such as temperature and humidity, was collected. This data along with the fan speed was logged to a cloud server over Wi-Fi using a Wi-Fi enabled microcontroller. The raw data consists of timestamp, temperature, humidity, and fan speed. The data is logged depending on the change of any parameter rather than a specific interval. The logged data is then visualized on the cloud server to monitor the usage patterns of the appliance and its subsequent energy consumption. The dataset is comprised of the fan data from the bedroom, living room, and lounge obtained by the resident's consent. This data is useful for data scientists, environmentalists, fan manufacturers, architects, social scientists, and several other field enthusiasts. The data can be analyzed based on monthly average temperature and humidity energy consumed, operational time per day or month and monthly/weekly summary of usage. Furthermore, by applying Artificial Intelligence (AI) algorithms on such data, it is feasible to extract patterns that indicate the appliance usage and identify changes in the daily routine. Many machine learning techniques can be applied on the dataset to introduce intelligent control of the appliance for adaptable operation without compromising on the comfort level of the user. |
format | Online Article Text |
id | pubmed-9876822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-98768222023-01-27 Dataset of usage pattern and energy analysis of an Internet of Things-enabled ceiling fan Khan, Hashim Raza Khalid, Muhammad Hashir bin Alam, Urooj Atiq, Mahnoor Qidwai, Uvais Qazi, Saad Ahmed Data Brief Data Article Many electrical appliances have progressed from sheer prototypes to viable products by being automated with the help of sensors and Internet of Things (IoT). In this data driven century, there aren't many data-centric solutions for the effective use of residential and commercial ceiling fans. For the said reason, sensors were installed on a remote-controlled BLDC ceiling fan, and a large amount of user data with environmental indicators such as temperature and humidity, was collected. This data along with the fan speed was logged to a cloud server over Wi-Fi using a Wi-Fi enabled microcontroller. The raw data consists of timestamp, temperature, humidity, and fan speed. The data is logged depending on the change of any parameter rather than a specific interval. The logged data is then visualized on the cloud server to monitor the usage patterns of the appliance and its subsequent energy consumption. The dataset is comprised of the fan data from the bedroom, living room, and lounge obtained by the resident's consent. This data is useful for data scientists, environmentalists, fan manufacturers, architects, social scientists, and several other field enthusiasts. The data can be analyzed based on monthly average temperature and humidity energy consumed, operational time per day or month and monthly/weekly summary of usage. Furthermore, by applying Artificial Intelligence (AI) algorithms on such data, it is feasible to extract patterns that indicate the appliance usage and identify changes in the daily routine. Many machine learning techniques can be applied on the dataset to introduce intelligent control of the appliance for adaptable operation without compromising on the comfort level of the user. Elsevier 2023-01-13 /pmc/articles/PMC9876822/ /pubmed/36710914 http://dx.doi.org/10.1016/j.dib.2023.108900 Text en © 2023 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Khan, Hashim Raza Khalid, Muhammad Hashir bin Alam, Urooj Atiq, Mahnoor Qidwai, Uvais Qazi, Saad Ahmed Dataset of usage pattern and energy analysis of an Internet of Things-enabled ceiling fan |
title | Dataset of usage pattern and energy analysis of an Internet of Things-enabled ceiling fan |
title_full | Dataset of usage pattern and energy analysis of an Internet of Things-enabled ceiling fan |
title_fullStr | Dataset of usage pattern and energy analysis of an Internet of Things-enabled ceiling fan |
title_full_unstemmed | Dataset of usage pattern and energy analysis of an Internet of Things-enabled ceiling fan |
title_short | Dataset of usage pattern and energy analysis of an Internet of Things-enabled ceiling fan |
title_sort | dataset of usage pattern and energy analysis of an internet of things-enabled ceiling fan |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876822/ https://www.ncbi.nlm.nih.gov/pubmed/36710914 http://dx.doi.org/10.1016/j.dib.2023.108900 |
work_keys_str_mv | AT khanhashimraza datasetofusagepatternandenergyanalysisofaninternetofthingsenabledceilingfan AT khalidmuhammadhashirbin datasetofusagepatternandenergyanalysisofaninternetofthingsenabledceilingfan AT alamurooj datasetofusagepatternandenergyanalysisofaninternetofthingsenabledceilingfan AT atiqmahnoor datasetofusagepatternandenergyanalysisofaninternetofthingsenabledceilingfan AT qidwaiuvais datasetofusagepatternandenergyanalysisofaninternetofthingsenabledceilingfan AT qazisaadahmed datasetofusagepatternandenergyanalysisofaninternetofthingsenabledceilingfan |