Predicting power for home appliances based on climatic conditions

Predicting power for home appliances based on climatic conditions PurposePower management in households has become the periodic issue for electric suppliers and household occupants. The number of electronic appliances is increasing day by day in every home with upcoming technology. So, it is becoming difficult for the energy suppliers to predict the power consumption for households at the appliance level. Power consumption in households depends on various factors such as building types, demographics, weather conditions and behavioral aspect. An uncertainty related to the usage of appliances in homes makes the prediction of power difficult. Hence, there is a need to study the usage patterns of the households appliances for predicting the power effectively.Design/methodology/approachPrincipal component analysis was performed for dimensionality reduction and for finding the hidden patterns to provide data in clusters. Then, these clusters were further being integrated with climate variables such as temperature, visibility and humidity. Finally, power has been predicted according to climate using regression-based machine learning models.FindingsPower prediction was done based on different climatic conditions for electronic appliances in the residential sector. Different machine learning algorithms were implemented, and the result was compared with the existing work.Social implicationsThis will benefit the society as a whole as it will help to reduce the power consumption and the electricity bills of the house. It will also be helpful in the reduction of the greenhouse gas emission.Originality/valueThe proposed work has been compared with the existing work to validate the current work. The work will be useful to energy suppliers as it will help them to predict the next day power supply to the households. It will be useful for the occupants of the households to complete their daily activities without any hindrance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Energy Sector Management Emerald Publishing

Predicting power for home appliances based on climatic conditions

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1750-6220
DOI
10.1108/IJESM-04-2018-0012
Publisher site
See Article on Publisher Site

Abstract

PurposePower management in households has become the periodic issue for electric suppliers and household occupants. The number of electronic appliances is increasing day by day in every home with upcoming technology. So, it is becoming difficult for the energy suppliers to predict the power consumption for households at the appliance level. Power consumption in households depends on various factors such as building types, demographics, weather conditions and behavioral aspect. An uncertainty related to the usage of appliances in homes makes the prediction of power difficult. Hence, there is a need to study the usage patterns of the households appliances for predicting the power effectively.Design/methodology/approachPrincipal component analysis was performed for dimensionality reduction and for finding the hidden patterns to provide data in clusters. Then, these clusters were further being integrated with climate variables such as temperature, visibility and humidity. Finally, power has been predicted according to climate using regression-based machine learning models.FindingsPower prediction was done based on different climatic conditions for electronic appliances in the residential sector. Different machine learning algorithms were implemented, and the result was compared with the existing work.Social implicationsThis will benefit the society as a whole as it will help to reduce the power consumption and the electricity bills of the house. It will also be helpful in the reduction of the greenhouse gas emission.Originality/valueThe proposed work has been compared with the existing work to validate the current work. The work will be useful to energy suppliers as it will help them to predict the next day power supply to the households. It will be useful for the occupants of the households to complete their daily activities without any hindrance.

Journal

International Journal of Energy Sector ManagementEmerald Publishing

Published: Sep 2, 2019

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