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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...

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Autores principales: Khan, Hashim Raza, Khalid, Muhammad Hashir bin, Alam, Urooj, Atiq, Mahnoor, Qidwai, Uvais, Qazi, Saad Ahmed
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
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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.
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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
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