Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis
This study examines the concept of demand response in household appliance use. Its primary aim is to explore the factors influencing electricity consumption behavior and employ K-means clustering to group households, estimating daily electricity consumption patterns. This understanding is essential...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024018383 |
| _version_ | 1827322348494127104 |
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| author | Timothy King Avordeh Samuel Gyamfi |
| author_facet | Timothy King Avordeh Samuel Gyamfi |
| author_sort | Timothy King Avordeh |
| collection | DOAJ |
| description | This study examines the concept of demand response in household appliance use. Its primary aim is to explore the factors influencing electricity consumption behavior and employ K-means clustering to group households, estimating daily electricity consumption patterns. This understanding is essential for the development of effective demand response strategies within the Greater Accra Region, Ghana. The research leveraged metrics, such as the Silhouette Score and principal component analysis to ensure the quality of the clustering process, effectively combining qualitative and quantitative data. Insights were enhanced by incorporating consumer behavior surveys to better comprehend appliance use trends and optimize demand response strategies. The findings emphasize differences in voltage, intensity, power consumption, and smart meter data among different household clusters. Notably, clusters 1 and 3 emerge as high energy consumers, particularly in water and cold appliances. These insights offer valuable guidance for targeted energy management and optimization strategies. This study underscores the significance of using consumer behavior insights to enhance and optimize demand response programs, providing essential guidance to energy stakeholders, particularly in Ghana, for the efficient optimization of electricity consumption and the successful implementation of demand response initiatives. |
| first_indexed | 2024-03-08T00:49:32Z |
| format | Article |
| id | doaj.art-eec5da95c05e4895ab0e19cddaef1c02 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:21:56Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-eec5da95c05e4895ab0e19cddaef1c022024-03-09T09:26:15ZengElsevierHeliyon2405-84402024-02-01104e25807Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysisTimothy King Avordeh0Samuel Gyamfi1Research and Consultancy Centre, University of Professional Studies, Accra (UPSA), P.O. Box 149, Legon, Accra, Ghana; Corresponding author.Regional Center for Excellence in Energy and Environmental Sustainability, School of Engineering, University of Energy and Natural, Resources (UENR), P.O. Box 214, Sunyani, GhanaThis study examines the concept of demand response in household appliance use. Its primary aim is to explore the factors influencing electricity consumption behavior and employ K-means clustering to group households, estimating daily electricity consumption patterns. This understanding is essential for the development of effective demand response strategies within the Greater Accra Region, Ghana. The research leveraged metrics, such as the Silhouette Score and principal component analysis to ensure the quality of the clustering process, effectively combining qualitative and quantitative data. Insights were enhanced by incorporating consumer behavior surveys to better comprehend appliance use trends and optimize demand response strategies. The findings emphasize differences in voltage, intensity, power consumption, and smart meter data among different household clusters. Notably, clusters 1 and 3 emerge as high energy consumers, particularly in water and cold appliances. These insights offer valuable guidance for targeted energy management and optimization strategies. This study underscores the significance of using consumer behavior insights to enhance and optimize demand response programs, providing essential guidance to energy stakeholders, particularly in Ghana, for the efficient optimization of electricity consumption and the successful implementation of demand response initiatives.http://www.sciencedirect.com/science/article/pii/S2405844024018383Home appliancesElectricity useConsumer behaviorK-center pointsResidential demand response |
| spellingShingle | Timothy King Avordeh Samuel Gyamfi Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis Heliyon Home appliances Electricity use Consumer behavior K-center points Residential demand response |
| title | Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis |
| title_full | Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis |
| title_fullStr | Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis |
| title_full_unstemmed | Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis |
| title_short | Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis |
| title_sort | optimizing residential demand response in ghana through iterative techniques and home appliance trend analysis |
| topic | Home appliances Electricity use Consumer behavior K-center points Residential demand response |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024018383 |
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